Digital Imaging and Communications in Medicine (DICOM): A Practical Introduction and Survival Guide, 2nd Edition

Digital Imaging and Communications in Medicine (DICOM)

Oleg S. Pianykh

Digital Imaging and Communications in Medicine (DICOM) A Practical Introduction and Survival Guide Second Edition

Oleg S. Pianykh, Ph.D. Harvard Medical School Beth Israel Deaconess Medical Center Department of Radiology Brookline Ave. 330 02215, Boston Massachusetts USA

ISBN 978-3-642-10849-5 e-ISBN 978-3-642-10850-1 DOI 10.1007/978-3-642-10850-1 Springer Heidelberg Dordrecht London New York Library of Congress Control Number: 2011939482 © Springer-Verlag Berlin Heidelberg 2012 This work is subject to copyright. All rights are reserved, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilm or in any other way, and storage in data banks. Duplication of this publication or parts thereof is permitted only under the provisions of the German Copyright Law of September 9, 1965, in its current version, and permission for use must always be obtained from Springer. Violations are liable to prosecution under the German Copyright Law. The use of general descriptive names, registered names, trademarks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. Product liability: The publishers cannot guarantee the accuracy of any information about dosage and application contained in this book. In every individual case the user must check such information by consulting the relevant literature. Printed on acid-free paper Springer is part of Springer Science+Business Media (www.springer.com)

To my parents

Preface: DICOM in Radiology

Digital Imaging and Communications in Medicine (DICOM) standard is the backbone of modern image display, equivalent only to film in the pre-digital era. Since the inception of this standard some 20 years ago, it has become the driving force behind the entire imaging workflow. DICOM truly controls all parts of digital image acquisition, transfer, and interpretation, and many radiologists and other imaging specialists and users may not realize to what extent their work relies on DICOM capabilities. We depend on DICOM extensively. We use DICOM to collect the original medical images. This process is much more complex compared to X-ray film but is also more accurate and complete. We use DICOM to send, distribute, and store images, irrespective of machine, manufacturer, or modality. DICOM controls proper image display, and without DICOM we would not have any image post-processing – whether it is simple multiplanar reconstruction, or more advanced perfusion analysis, virtual colonoscopy, volume segmentation, or computer-aided diagnosis. We owe to DICOM much of the ease and flexibility that we enjoy in our work. As digital medicine becomes increasingly complex and imaging projects globalize, knowledge of DICOM basics for any healthcare professional becomes crucial. Whether you are thinking about your next PACS upgrade, or a new teleradiology project, or simply about installing a new digital modality, DICOM should be the crucial reference. Standard, DICOM-based workflow is the only way to build a robust and efficient radiology practice. It is also the only way to integrate your medical imaging into a complete enterprise-wide electronic patient record solution. This book, written from a hands-on angle, gives the healthcare professional a comprehensive introduction to the evolving and multifaceted standard that is DICOM. It may become your daily reference, a teaching guide, or a tool to prepare you for dealing with the intricacies of original DICOM volumes and supplements. Take full advantage of it, and take full advantage of DICOM. Vassilios Raptopoulos, M.D. Vice Chair of Clinical Services Beth Israel Deaconess Medical Center Professor of Radiology, Harvard Medical School vii

Preface: DICOM in Healthcare IT

Unlike healthcare standards such as HL7, the DICOM standard is not as well known or understood by IT professionals. On one hand, DICOM stands and delivers on the promise of interoperability and cost effectiveness. On the other hand, it requires some tweaking to work. DICOM planning and implementation errors are common, often resulting in operational nightmares. Given that digital medicine and PACS require a substantial investment and are critical, getting the DICOM part right up front is essential. Frequently overlooked is the impact of DICOM and PACS on the actual patient. DICOM drives clinical workflow from the moment a patient enters Radiology, collecting important imaging data from the digital modality and delivering it to the radiologist in the most accurate and diagnostically complete form within minutes. Comparing previous studies with the current study is effortless with DICOM, resulting in more skillful diagnosis and treatment. No film to lose, misplace, under- or overexpose, and fewer repeated studies – all add up to faster and vastly improved healthcare. DICOM is beneficial to the patient, provider, and the facility; and IT professionals owe it to them to get it right. The importance of DICOM as a working, integral part of the IT infrastructure will continue to expand. The number of present and available DICOM and PACS applications is staggering. Selecting the PACS right for you becomes more challenging with each passing year and a good understanding of DICOM is now essential. Without a basic and practical understanding of DICOM you risk making embarrassing mistakes in such areas as security, equipment specification, capacity planning, network configuration, and disaster recovery/continuity of operations. The built-in security of DICOM is not going to be adequate to address HIPAA requirements and will need to be augmented. The various modalities have different image resolutions, which affect choice of viewing monitors. DICOM supports several image compression technologies. The tradeoff between image sizing and quality will affect equipment sizing and network bandwidth requirements. The DICOM connectivity model is still very much static IP centric. Remote access through VPN needs to be carefully planned. The complexity and interactions of all of the above are interrelated and can make upgrades difficult. The aspect of disaster recovery and continuity of operations emerge as so technically challenging that they are frequently ix

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Preface: DICOM in Healthcare IT

ignored. The practical way of dealing with these issues begins with an understanding of DICOM. In the following pages you will not have to wade and sift through volumes of technical specifications to get the knowledge you need. Consider it your one-stopshopping experience for a good understanding of DICOM. New Orleans, LA

David Troendle Assistant Vice Chancellor for Information Technology LSU Health Sciences Center

Who Needs This Book?

If you are involved in any form of medical work, decision making, image analysis, or research and development, this book is for you. As “digital” spreads even into the most veiled areas of contemporary medicine, understanding DICOM becomes increasingly imperative for ensuring accuracy, efficiency, and reliability of any medical application or process. DICOM is an extremely powerful tool when you can harness its power. But it could just as easily be the doom of an ill-planned project. Helping you learn DICOM was the principal reason for writing this book. A lesser, but no less important, reason was the sheer repetitiveness of DICOM problems that I learned to deal with personally as a medical IT administrator, researcher, and developer. To a great extent this book has evolved from my personal “DICOM diary,” which I started writing to record the problems and the solutions of my countless experiences. A seemingly inexhaustible list of DICOM-related questions is inevitably asked wherever digital medicine is practiced; and many practitioners continue to fall into numerous DICOM traps as they attempt to bring new equipment and applications online. Although it would be too ambitious on my part to protect you from all possible DICOM traps, together we will try to identify and avoid the most common ones. Finally, DICOM – being a very complex and evolving standard – is not exactly fun to read. On the other hand, many popular overviews tend to be sketchy and introductory. In this book, I try to help you navigate between the Scylla and Charybdis of simple to understand and important to know. Do not expect the journey to be easy, but rest assured that it will be rewarding. You will become familiar with the principal DICOM concepts and terminology; you will see how they fit together; and you will gain sufficient knowledge to start dealing with DICOM in all its complexity. Your next best resource after reading this book is the DICOM standard itself – then you will be prepared for the rest of your own DICOM Odyssey. I hope that this educational foray will be entertaining, informative, and will change the way you look at your work. So let’s go!

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Acknowledgments

Knowledge is always shared, and many things I described in this book I learned with the help of others. It was my pleasure to work with many radiologists, researchers, computer scientists, technicians, engineers, and students – listing all of their names would be virtually impossible. I would like to thank all of them for their time and patience. I would also like to thank: • My dear, wonderful family. • The faculty and students of Harvard Medical School, BIDMC Radiology, and LSUHSC Radiology, from whom I learned so much. • The countless clients I worked with. • David Troendle, for his very valuable comments and discussions on this book; Oliver Lenhart, for his helpful suggestions; John Tyler, for his lessons. • Roger Cote for turning this book into something that not only the author can understand. • You, my dear readers, for taking the time. Your feedback will be very helpful, and you can always reach me at [email protected]. I hope this book will make your work easier and your goals higher. New Orleans, USA– Moscow, Russia– Boston, USA

Best regards Oleg Pianykh

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Contents

Part I

Introduction to DICOM

1 What Is DICOM? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

3

2 How Does DICOM Work? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

7

3 Where Do You Get DICOM from? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.1 DICOM Versus Digital . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2 DICOM, DICOM-Compatible, DICOM-Ready? . . . . . . . . . . . . . . . . . 3.3 In the Middle of Nowhere . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

11 11 13 14

Part II

DICOM and Clinical Data

4 Brief History of DICOM . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.1 How Did This All Get Started? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

19 21

5 Parlez-vous DICOM? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.1 IT Boot Camp . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.2 Text Versus Binary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.3 DICOM Grammar: VRs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.3.1 VR Length . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.3.2 Characters: Foreign and Wild . . . . . . . . . . . . . . . . . . . . . . . . . . 5.3.3 Text VRs: CS, SH, LO, ST, LT, UT . . . . . . . . . . . . . . . . . . . . . . 5.3.4 Dates and Times: DA, TM, DT, AS . . . . . . . . . . . . . . . . . . . . . . 5.3.5 Numbers in Text Format: IS, DS . . . . . . . . . . . . . . . . . . . . . . . . 5.3.6 Numbers in Binary Format: SS, US, SL, UL, FL, FD, OB, OW, OF, AT . . . . . . . . . . . . . . . . . . . . . . 5.3.7 PN – Storing Person’s Names . . . . . . . . . . . . . . . . . . . . . . . . . . 5.3.8 AE – Naming Application Entities . . . . . . . . . . . . . . . . . . . . . . 5.3.9 UI – Unique Identifiers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.3.10 SQ – Sequencing Data Sets . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.3.11 UN – Representing Unknown Values . . . . . . . . . . . . . . . . . . . . 5.4 DICOM Data Dictionary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.4.1 Standard DICOM Data Dictionary . . . . . . . . . . . . . . . . . . . . . .

27 27 29 31 36 37 38 38 39 39 40 41 41 41 43 43 43 xv

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5.4.2 Private DICOM Data Dictionaries . . . . . . . . . . . . . . . . . . . . . . . 5.4.3 Standard DICOM Command Dictionary . . . . . . . . . . . . . . . . . . 5.5 DICOM Objects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.5.1 Encoding Data Elements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.5.2 Encoding Data Groups . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.5.3 Example: Element and Group Lengths . . . . . . . . . . . . . . . . . . . . 5.5.4 Encoding DICOM Data Objects . . . . . . . . . . . . . . . . . . . . . . . . . 5.5.5 SQ: Encoding DICOM Object Sequences . . . . . . . . . . . . . . . . . . 5.5.6 Required and Optional Data Elements . . . . . . . . . . . . . . . . . . . . 5.5.7 Storing Image Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.5.8 Unique Identifiers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.6 DICOM Information Hierarchy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.6.1 Problems with Patient ID . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.6.2 Problems with Study, Series, and Image UIDs . . . . . . . . . . . . . . 5.6.3 Hierarchical and Relational Data . . . . . . . . . . . . . . . . . . . . . . . . . 5.7 Modules, IODs and IEs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.7.1 Attribute Macros: Making It Easier . . . . . . . . . . . . . . . . . . . . . . . 5.7.2 Information Modules: Basic Data Blocks . . . . . . . . . . . . . . . . . . 5.7.3 Information Entities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.7.4 DICOM Information Objects . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.7.5 IODS and Their Instances . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.7.6 Learning More . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

47 47 48 49 53 55 56 57 62 62 64 66 68 70 71 71 72 73 77 77 77 80

6 Medical Images in DICOM . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.1 DICOM Bitmaps . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.2 Image Compression . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.2.1 Lossless Compression . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.2.2 Lossy Compression . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.2.3 Streaming Compression . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.2.4 Choosing the Right Compression Technique. . . . . . . . . . . . . . . . 6.3 Working with Digital Medical Images. . . . . . . . . . . . . . . . . . . . . . . . . . 6.3.1 Image Interpolation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.3.2 Image Reconstructions. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.3.3 Grayscale Depth. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.3.4 Waveforms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.3.5 Overlays . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.3.6 Supporting True Video in DICOM . . . . . . . . . . . . . . . . . . . . . . .

81 82 86 88 88 91 93 97 97 100 102 107 108 111

Part III

DICOM Communications

7 DICOM SOPs: Basic. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.1 Identifying Units on the DICOM Network . . . . . . . . . . . . . . . . . . . . . . 7.2 Services and Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.2.1 DIMSE Services . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.2.2 Simple DIMSE Example: C-Echo . . . . . . . . . . . . . . . . . . . . . . . .

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7.2.3 SOP: Service-Object Pairs . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.2.4 Verification SOP . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.3 Storage . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.3.1 C-Store IOD . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.3.2 C-Store DIMSE . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.4 Query: Find . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.4.1 A Few Words on Data Matching in DICOM . . . . . . . . . . . . . . 7.4.2 C-Find IOD . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.4.3 C-Find DIMSE . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.5 Modality Worklist . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.5.1 MWL IOD . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.5.2 MWL DIMSE . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.6 Basic DICOM Retrieval: C-Get . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.6.1 C-Get IOD . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.6.2 C-Get DIMSE . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.7 Advanced DICOM Retrieval: C-Move . . . . . . . . . . . . . . . . . . . . . . . . 7.7.1 C-Move IOD . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.7.2 C-Move DIMSE . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.7.3 C-Move Versus C-Get . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.8 C-Cancel – Cancel Them All! . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.9 DICOM Ping, Push, and Pull . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.10 Gentleman’s Toolkit . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.11 Matching Application Roles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

129 130 132 132 133 137 138 140 143 146 148 149 150 150 154 154 157 158 160 163 164 164 165

8 DICOM SOPs: Beyond Basic . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.1 Storage Commitment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.2 Secondary Capture . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.3 Structured Reports . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.4 Encapsulated PDFs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.5 Hardcopy Printing. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

169 169 170 171 173 174

9 DICOM Associations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9.1 Association Establishment Basics . . . . . . . . . . . . . . . . . . . . . . . . . . . 9.2 Association Establishment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9.3 Abstract Syntax . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9.4 Transfer Syntax. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9.5 Application Context . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9.6 Presentation Context . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9.7 User Information . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9.8 Protocol Data Unit (PDU) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9.8.1 A-Associate-RQ . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9.8.2 A-Associate-AC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9.8.3 A-Associate-RJ . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9.8.4 A-Abort. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9.8.5 A-Release-RQ, A-Release-RP . . . . . . . . . . . . . . . . . . . . . . . . . 9.8.6 P-Data-TF . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

177 178 180 180 184 187 188 191 193 195 196 197 198 199 200

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9.8.7 How Associations Terminate . . . . . . . . . . . . . . . . . . . . . . . . . . 9.8.8 Communication Overhead? . . . . . . . . . . . . . . . . . . . . . . . . . . . 9.9 What Do I Do When It Fails? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9.10 Point-to-Point Spell . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9.11 Networking: Standard and Beyond . . . . . . . . . . . . . . . . . . . . . . . . . . Part IV

201 201 204 206 209

DICOM Media and Security

10 DICOM Media: Files, Folders, and DICOMDIRs. . . . . . . . . . . . . . . . . 10.1 DICOM File Format . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10.1.1 Preamble and DICM Prefix . . . . . . . . . . . . . . . . . . . . . . . . . . 10.1.2 Group 0002: DICOM File Meta Information . . . . . . . . . . . . . 10.1.3 Data Object . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10.1.4 DICOM File IDs and Names . . . . . . . . . . . . . . . . . . . . . . . . . 10.2 Special DICOM File Formats . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10.2.1 DICOMDIR . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10.2.2 Secure DICOM File Format . . . . . . . . . . . . . . . . . . . . . . . . . . 10.3 DICOM File Services. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10.3.1 DICOM File Set . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10.3.2 File Management Roles and Services. . . . . . . . . . . . . . . . . . . 10.4 Grains of Salt . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10.4.1 DICOMDIR . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10.4.2 Media Storage . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10.4.3 Please, Send us Some Film! . . . . . . . . . . . . . . . . . . . . . . . . . . 10.4.4 Just Suggesting… . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10.4.5 Export and Import. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10.5 Storing DICOM Data in PACS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10.5.1 File-Based PACS Storage . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10.5.2 Database-Based PACS Storage . . . . . . . . . . . . . . . . . . . . . . . . 10.5.3 Mixed PACS Storage Models . . . . . . . . . . . . . . . . . . . . . . . . . 10.5.4 Choice of Internal File Format . . . . . . . . . . . . . . . . . . . . . . . .

217 218 218 219 219 221 222 222 228 229 229 230 232 232 234 235 236 238 238 240 240 241 242

11 DICOM Security . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.1 DICOM Hacking . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.2 DICOM Anonymization. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.3 Understanding Encryption . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.3.1 The Main Idea Behind Encryption . . . . . . . . . . . . . . . . . . . . . 11.3.2 Encrypting the Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.3.3 Verifying Data Integrity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.3.4 Validating Data Origin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.4 Encrypting DICOM Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.4.1 De- and Re-Identification: PS3.15 . . . . . . . . . . . . . . . . . . . . . 11.4.2 De- and Re-Identification: Supplement 142 . . . . . . . . . . . . . . 11.4.3 Security and DICOM Images . . . . . . . . . . . . . . . . . . . . . . . . . 11.5 Concluding Remarks on Security. . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.6 What Else? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

243 243 245 250 250 252 253 254 255 255 258 259 260 262

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12 Incompatibility of Compatible. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12.1 DICOM Conformance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12.2 Testing, Testing, and Yes, More Testing . . . . . . . . . . . . . . . . . . . . . . 12.3 Who Wants DICOM? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12.4 DICOM from a Black Box . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12.5 “Home-Made” DICOMs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12.6 Open-Source DICOM . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12.7 DICOM Validation Tools . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Part V

267 267 268 270 271 272 273 277

Advanced Topics

13 DICOM and Teleradiology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13.1 Can I See the Image? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13.2 You’ve Got Mail . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13.3 Teleradiology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13.4 DICOM and WWW . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13.5 DICOM Email . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13.6 Vox Populi – Vox Dei . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13.7 From PACS to PDAs? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13.7.1 “Pocket Radiology” . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13.7.2 Multimedia Expansion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13.7.3 Thin and Thick DICOM . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13.7.4 New Gadgets, Old Technology? . . . . . . . . . . . . . . . . . . . . . . . 13.8 Starting Your Teleradiology Project . . . . . . . . . . . . . . . . . . . . . . . . . . 13.9 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

281 281 284 286 292 296 300 303 303 306 308 311 313 316

14 Standards and System Integration in Digital Medicine . . . . . . . . . . . . 14.1 HL7: HIS and RIS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14.2 Integration Problems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14.2.1 Data Integration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14.2.2 Application Integration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14.3 IHE: Integration Profiles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

319 320 323 325 326 327

15 Disaster PACS Planning and Management . . . . . . . . . . . . . . . . . . . . . . 15.1 What It Takes to Kill a PACS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15.2 Extreme PACS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15.2.1 Digging in the Dirt . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15.2.2 Confidentiality and Security . . . . . . . . . . . . . . . . . . . . . . . . . . 15.2.3 Archiving . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15.2.4 Disaster-Proof PACS Design . . . . . . . . . . . . . . . . . . . . . . . . .

331 332 336 336 339 339 340

16 DICOM Applications: Usual and Not . . . . . . . . . . . . . . . . . . . . . . . . . . . 16.1 Temporal Image Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16.2 DICOM Localization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16.3 Radiation Dose Monitoring . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16.4 DICOS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16.5 Coloring DICOM Images: Science and Art . . . . . . . . . . . . . . . . . . . .

343 343 344 350 355 357

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Contents

17 DICOM Software Development. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17.1 Developing DICOM from Scratch . . . . . . . . . . . . . . . . . . . . . . . . . . . 17.2 The “I Just Need to Open this DICOM File” Project. . . . . . . . . . . . . 17.3 Implementing DICOM . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17.4 Learning from Others . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17.5 What’s Next? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

363 363 364 364 369 370

18 DICOM Implementation Plans . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18.1 Imaging Center . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18.2 Teleradiology Center . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18.3 Hospital . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18.4 Image-Processing Lab . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

373 373 374 376 377

19 DICOM FAQs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19.1 Frequent Problems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19.1.1 I Want to Go Digital. How Do I Start? . . . . . . . . . . . . . . . . . 19.1.2 How Do I Distinguish DICOM Files from Others? . . . . . . . 19.1.3 I Am Trying to Open a File in My DICOM Program, and It Does Not Open . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19.1.4 What Is DICOMDIR?. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19.1.5 I Send a Study to My DICOM Device, but It Never Gets There . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19.1.6 I Send a Study to My PACS Archive, It Arrives There, but Gets Merged with Another Study (Patient) . . . . . . . . . . 19.1.7 I Need to Send This Study to a Remote Facility . . . . . . . . . 19.1.8 I Open a DICOM Image In My Software, and It Looks Wrong . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19.1.9 I Do Some Image Editing on My PACS, but When I Send It to Another System, the Editing Is Gone. . . . 19.1.10 Can I Break My DICOM Device If I DICOM-Connect It to Another? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19.2 Naïve Questions That MDs Like to Ask and Salesmen Like to Brag About . . . . . . . . . . . . . . . . . . . . . . . . . . . 19.2.1 Do I Get the Original Image Resolution If I Buy Your PACS? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19.2.2 Is Your System Web-based? . . . . . . . . . . . . . . . . . . . . . . . . . 19.2.3 Can You Connect to My CT Scanner (MR, US, PET, and So On)? . . . . . . . . . . . . . . . . . . . . . . . . . 19.2.4 Do You Have 3D Imaging (Perfusion, Diffusion, Volume Measurements, and So On)? . . . . . . . . . . . . . . . . . . 19.2.5 Does Your System Support Multitasking (Multithreading, 64-Bit Processors, and So On, and So On)? . . . . . . . . . . . . . 19.2.6 Can I Use Your System for Teleradiology? . . . . . . . . . . . . . 19.2.7 How Fast Can You Transfer the Images? . . . . . . . . . . . . . . . 19.2.8 How Much Data Can I Store In Your PACS? . . . . . . . . . . . . 19.2.9 Is Your DICOM Software Secure? . . . . . . . . . . . . . . . . . . . .

381 381 381 381 382 382 383 383 383 383 384 384 385 385 385 385 386 386 387 387 388 388

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19.2.10 Do You Have Full-Fidelity DICOM? . . . . . . . . . . . . . . . . . . 389 19.2.11 Do You Have Free DICOM? . . . . . . . . . . . . . . . . . . . . . . . . . 389 Appendix . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 391 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 411 Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 413

Part I Introduction to DICOM

1

What Is DICOM?

When working toward the solution of a problem, it always helps if you know the answer – Murphy’s Law

You can walk with this question into most modern, digital, state-of-the-art hospitals and spend hours looking for someone who could answer it correctly. We all get used to buzz words and acronyms, and rarely think about their meanings. Unfortunately, nothing distances you more from success than not knowing what you are dealing with! DICOM stands for Digital Imaging and COmmunications in Medicine and represents years of effort to create the most universal and fundamental standard in digital medical imaging. As such, it provides all the necessary tools for diagnostically accurate representation and processing of medical imaging data. Moreover, contrary to popular belief, DICOM is not just an image or file format. It is an all-encompassing data transfer, storage and display protocol built and designed to cover all functional aspects of contemporary medicine (which is why many view DICOM as a set of standards, rather than a single standard). Without a doubt, DICOM truly governs practical digital medicine. Another important acronym that seemingly all DICOM vendors plug into their names is PACS (Picture Archiving and Communication Systems). PACS are medical systems (consisting of necessary hardware and software) built to run digital medical imaging. They comprise: 1. Modalities: Digital image acquisition devices, such as CT scanners or ultrasound. 2. Digital image archives: Where the acquired images are stored. 3. Workstations: Where radiologists view (“read”) the images. When you play with your digital camera (modality), store the images on your computer (archive), and send them to your friends (reviewers), you use the exact same model (Fig. 1.1). PACS are directly related to DICOM: they are the standard incarnate. Their functionality is DICOM-driven, which guarantees their interoperability. However, each DICOM unit has its own purpose, implementing only a subset of DICOM required for the task. For that reason, any PACS device or software comes with its own O.S. Pianykh, Digital Imaging and Communications in Medicine (DICOM ), DOI 10.1007/978-3-642-10850-1_1, © Springer-Verlag Berlin Heidelberg 2012

3

4

1  What Is DICOM? Modalities

Workstations

Archive

Acquire

Store

View

Fig. 1.1  Major PACS components: image acquisition devices (modalities) store images on a digital archive. From there images are accessed by radiologists at the viewing workstations

DICOM Conformance Statement – a very important document explaining the extent to which the device supports the DICOM standard.1 In this way, a digital CT scanner uses DICOM to acquire and distribute computed tomography images; a DICOM printer to print; a DICOM archive to store and query DICOM data; and so on. One can hardly imagine modern digital medicine without DICOM and PACS. The DICOM standard – conceived over 20 years ago – plays an integral role in the digital medicine evolution, ensuring the highest diagnostic standards and the best performance. DICOM truly shaped the landscape of contemporary medicine by providing: 1. Universal standard of digital medicine. All current digital image acquisition devices produce DICOM images and communicate through DICOM networks. Modern medical workflow is implicitly controlled by a multitude of DICOM rules, which we will review in this book.

1

 S3.2 part of DICOM standard covers conformance statements, and provides a detailed conforP mance statement template (Appendix) with samples.

1  What Is DICOM?

5

2. Excellent image quality. Just to give you an example, DICOM supports up to 65,536 (16 bits) shades of gray for monochrome image display, thus capturing the slightest nuances in medical imaging. In comparison, converting DICOM images into JPEGs or bitmaps (limited to 256 shades of gray) often renders the images unacceptable for diagnostic reading. DICOM takes advantage of the most current and advanced digital image representation techniques to provide the utmost diagnostic image quality. 3. Full support for numerous image-acquisition parameters and different data types. Not only does DICOM store the images, it also records a legion of other image-related parameters such as patient 3D position, sizes and orientations, slice thickness, radiation doses and exposures, image processing filters, and so on. This data immensely enriches the informational content of DICOM images, and facilitates processing and interpreting the image data in various ways (for example, creating 3D images from several sequences of 2D CT slices). 4. Complete encoding of medical data. DICOM files and messages use more than 2000 standardized attributes (defined in the DICOM Data Dictionary) to convey various medical data from patient name to image color depth to current patient diagnosis. Often essential for accurate diagnostics, the data captures all aspects of the current radiology. 5. Clarity in describing digital imaging devices and their functionality – the backbone of any medical imaging project. DICOM defines medical device functionality in very precise and device-independent terms. Working with medical devices through their DICOM interfaces becomes a straightforward, predictable process leaving little room for errors. At the time this book was written, the DICOM standard consisted of 16 volumes (from 1 to 18, volumes 9 and 13 being retired) known as parts, and traditionally numbered from PS3.1 to PS3.18.2 We used the last publicly available revision of the standard, performed in 2009.3

2 3

Number 3 representing DICOM 3.0, the current version of the standard. See DICOM home page at NEMA’s web site, http://medical.nema.org.

2

How Does DICOM Work?

Everything in life is important, important things are simple, simple things are never easy – Murphy’s Law

To introduce order into the complex medical environment, DICOM uses its own lingo, based on its model of the real world (DICOM information model). Here is that model in a nutshell. All real-world data – patients, studies, medical devices, and so on – are viewed by DICOM as objects with respective properties or attributes.1 The definitions of these objects and attributes are standardized according to DICOM Information Object Definitions (IODs). Think about IODs as collections of attributes, describing IOD properties. A Patient IOD, for example, can be described by patient name, ID, sex, age, weight, smoking status, and so on – as many attributes as needed to capture all clinically relevant patient information. As the famous “philosopher” Popeye was fond of saying: “I ams what I ams and that’s all that I ams.” Bearing that wisdom in mind, a patient (just like any other DICOM object) is the set of attributes of which he consists, as you can see in Fig. 2.1. DICOM maintains a list of all standard attributes (more than 2,000 of them), known as the DICOM Data Dictionary, to ensure consistency in attribute naming, formatting, and processing. For example, our patient attributes – name, date of birth, sex, and so on – are included in the DICOM Data Dictionary as well. All DICOM attributes are formatted according to 27 Value Representation (VR) types which include dates, times, names, identifiers, and so on. As soon as the data is captured as DICOM data attributes, it can be transmitted and processed between various DICOM devices and software – known as Application Entities (AEs) in DICOM. DICOM represents this processing with a servicerendering model: Application Entities provide services to each other (Fig. 2.2). Because each service usually involves some data exchange (typically performed over a computer network) it becomes natural to associate particular 1

If you have an IT background, you will certainly recognize object-oriented design.

O.S. Pianykh, Digital Imaging and Communications in Medicine (DICOM ), DOI 10.1007/978-3-642-10850-1_2, © Springer-Verlag Berlin Heidelberg 2012

7

8

2 Patient in Real World

How Does DICOM Work?

Patient object in DICOM World

Name

John Smith

ID

123456

DOB

19681108

Weight

75.5

Sex

M

Patient IOD (Information Object Definition)

…….

Fig. 2.1 From real data to DICOM IODs. Each IOD is a collection of attributes

MR scanner Mobile workstation

CT scanner 1. Digital Archive software PDA with DICOM viewer

2. DICOM image processing software Ultrasound

Fig. 2.2 DICOM Application Entities. Note that Application Entities can be programs, so multiple AEs can be running on the same device, as shown for the archive server

service types with the data (IODs) that they process. DICOM calls these associations Service-Object Pairs (SOPs), and groups them into SOP Classes. For example, storing a computed tomography (CT) image from a digital CT scanner to a digital PACS archive corresponds to the CT Storage Service-Object Pair (SOP), as shown in Fig. 2.3. In this particular example, the CT image represents the DICOM Information Object Definition (Computer Tomography IOD). The CT scanner requests the CT

2

How Does DICOM Work?

9

CT Storage SCU (Example: CT scanner) Sends CT images to be stored (requests CT Storage service)

Accepts CT images for storage (provides CT Storage service)

CT Storage SCP (Example: Archive)

Fig. 2.3 DICOM services

Accepting CT images for storage as CT Storage SCP

Printing CT images as DICOM Print SCU

Fig. 2.4 Different SCP and SCU processing on the same AE

storage service from the archive, and the archive provides the CT storage service to the scanner. Therefore, DICOM calls the service requestors Service Class Users (SCUs) and the service providers Service Class Providers (SCPs). In the same CT example, the CT scanner acts as the CT Storage Service Class User (SCU), and the digital archive as the CT Storage Service Class Provider (SCP). Everything is relative, and SCU/SCP roles may change depending on the processing logic. Imagine that the same digital archive needs some of its images to be printed on a DICOM printer. In this case, the archive acts as an SCU and the printer as an SCP (Fig. 2.4). Each data exchange between SCU and SCP peers is called association. Consequently, each network transfer begins with Association Establishment – the DICOM handshake-used when the two connecting applications exchange

10

2

How Does DICOM Work?

information about each other. This information is called Presentation Contexts. If the two applications can match their contexts, they can connect and start SCU-SCP processing. In our example in Fig. 2.3, the CT scanner will open association to the Archive with CT image storage presentation context. The Archive will check its current settings – which may include storing different types of images such as MR or X-rays – and if CTs are among them, it will accept the scanner association. Because hundreds of DICOM devices and applications are produced by hundreds of DICOM manufacturers, each DICOM unit will be accompanied by its own DICOM Conformance Statement. This statement explains which SOPs (services) the unit supports, and to what extent (SCU, SCP, or both) it supports them. The DICOM Conformance Statement is your most essential planning guide for any DICOM-related project. Obtain it from the manufacturer ahead of time and read it carefully. For example, if you buy a digital archive that supports CT Storage SCU only (does not support CT Storage SCP), you won’t be able to store CT images in it. The archive won’t be able to provide the CT storage service. This brief summary reflects the core DICOM functionality, and as you can see, it is quite straightforward. In fact, understanding the theory of DICOM is easy; dealing with DICOM in real life is often the challenge. Most of this book is committed to helping you meet that challenge.

3

Where Do You Get DICOM from?

If anything can go wrong, it will – Murphy’s Law

The DICOM standard is free and can be found on the official DICOM website (http://medical.nema.org) maintained by the National Electrical Manufacturers Association (NEMA). However, from a practical perspective, you usually deal with DICOM implementations in devices and software. Currently, hundreds of these products are on the market constantly transforming and vying for your attention. This natural selection, however, only adds another layer of commonly misunderstood and confusing concepts to the original complexity of the DICOM standard. Moreover, the question “Are you completely DICOM-compatible?” can never be answered with a simple Yes or No. In fact, this question is simply inaccurate. All DICOM devices and software support only specific parts of the DICOM standard required for their functionality. As a result of this selectiveness, implementing a DICOM workflow that is completely DICOM-compatible is tricky business, making it one of the most critical and treacherous phases in organizing any medical imaging practice. So, just how do you introduce DICOM into your practice and what should you avoid? Well, let’s start with the basics.

3.1

DICOM Versus Digital

Like all human beings, we perceive the world around us in analog mode – that is, as continuous shapes and shades. Computers on the other hand function in digital mode – they store and process images pixel-by-pixel, number-by-number. DICOM, as its very name suggests, also deals only with digital images. Digital images can be acquired in this manner (computed tomography being an example), or digitized from analog (consider scanned patient reports). In any case, acquiring or converting images into digital format is the very first step required for any DICOM implementation. O.S. Pianykh, Digital Imaging and Communications in Medicine (DICOM ), DOI 10.1007/978-3-642-10850-1_3, © Springer-Verlag Berlin Heidelberg 2012

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Where Do You Get DICOM from?

All contemporary image-acquisition modalities provide digital image output. Such modalities include computed tomography (CT), magnetic resonance (MR), ultrasound (US), nuclear medicine (NM), and more. By their nature, some of these devices have always acquired images digitally; others were complemented with digitizing circuits as technology progressed. The problem, however, is that while DICOM implies digital, digital does not guarantee DICOM. You could very well purchase a digital medical imaging device that has no DICOM support whatsoever. You are not making DICOM images with your point-and-shoot camera, are you? How would you know if you can use DICOM with your medical imaging device? It is easy: as I already mentioned, any DICOM device should come with a DICOM Conformance Statement.1 The Conformance Statement, in many cases, is more important than the device’s user manual and should be supplied by the device manufacturer to outline the DICOM functions supported by the device. For this very reason, the statement should leave no room for errors or false assumptions about the device’s functionality. You should be conjuring the guardian spirits of DICOM Conformance Statements any time you plan or perform any DICOM-related installation. Always ask for it, and always make sure that you have the correct version for your model. Even the same device or software can come in a variety of revisions, and your Conformance Statement should correspond to the version that you are using. Upgrade wisely! Blame It on the New Guys?

Keeping up with DICOM upgrades can be an expensive and workflow-critical proposition. I know of a hospital that used to have a nice DICOM printer from a well-known company – I’ll call them A – that was set up to print CT images from a DICOM-compatible scanner, produced by another well-known company – I’ll refer to as B. When the scanner aged enough to go up in smoke (literally), B replaced it with a brand new model; only to discover that it could not print to A’s printer. Did I forget to mention that all devices were DICOM-compliant? So the hospital IT staff contacted A and the printer company recognized that the printer software version was old (circa Windows NT) and had eminent bugs. They said that it was no longer supported and beyond repair; the only solution was an upgrade – for a reasonable fee of course. What did the hospital IT do? Complained to B for breaking their DICOM network, and got a new DICOM printer from them – for free. The moral of the story? When you run out of money, just learn to be more … um … creative.

1

Sorry for beating you over the head with countless references to these documents; you will see them again and again as we continue. But because they are so important in any DICOM project, I implore you to forgive me for the browbeating.

3.2

3.2

DICOM, DICOM-Compatible, DICOM-Ready?

13

DICOM, DICOM-Compatible, DICOM-Ready?

Let’s say that you have identified your imaging needs and you are ready to purchase a DICOM unit. Is there anything you need to know before writing that check? Definitely. Buying a DICOM-compatible device or software application is like having dinner in an expensive restaurant. Your device manufacturer presents you with a wonderful entrée (the device), and the option to add a fancy decadent dessert (the entire DICOM functionality). Perhaps, to save on your budgetary diet, you think about passing on dessert. Many clinical practitioners have committed this very mistake: skipping what is presented as optional to save a little and not ordering what they really should have – and found themselves eating crow in the end. You might ask: “Why should we pay for DICOM functionality as an additional DICOM device option when having a DICOM device without DICOM is nearly meaningless?” Well, it might be meaningless to you, but it surely is not meaningless to the device manufacturers. To better comprehend this paradox, let’s recall what we learned in the previous section. All current, medical image-acquisition devices are digital, which does not necessarily mean that they inherently provide DICOM functionality. Device manufacturers can implement their own proprietary protocols for digital image acquisition, storage, and display. Those manufacturer-dependent protocols will suffice to allow the device to operate as a standalone unit, or possibly connected to other units from the same manufacturer. In other words, the device will work as expected, but only inside its little proprietary shell. When manufacturers use proprietary protocols, it results in convenience and clever marketing – for them. Convenience, because the manufacturers can do anything they want to implement the device functionality internally – they are not limited by any standard or any particular requirement. Marketing, because proprietary standards make you dependent on your manufacturer – you simply cannot connect to another vendor’s equipment or software. This might sound like just another conspiracy theory, but pragmatically any proprietary format inevitably segments the medical device market. We will discuss this in more detail toward the end of the book (see Chap. 12). The bottom line is that in many cases, you must explicitly purchase device DICOM functionality as an option (in addition to the DICOM device purchase itself). If you do not purchase the DICOM functionality option, you end up with what some manufacturers label as a “DICOM-Ready” device – a device that could have been running DICOM, if you had paid for it. Another way of looking at this problem is to realize that, in its very essence, DICOM is a device-interfacing standard. It connects the devices externally, but it is not meant to drive them internally. DICOM provides a standard for device output (which also ensures that various devices can be connected to each other). When you

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Where Do You Get DICOM from?

purchase a DICOM option, you pay for this conversion, you pay for uniformity, and you pay for the ability to export and process device data in the standard format. If you are serious about your medical imaging workflow, all DICOM options on your manufacturer’s price list should be treated as mandatory. What should you do if any of these options are missing? Heed these words of wisdom: despite the urgency and possible sense of guilt, never attempt to fix this problem yourself. If you have a DICOM-Ready device that is lacking DICOM options, the only way to remedy the problem is to contact the device manufacturer. Resolving all DICOM issues with the manufacturer should become your standard approach under any circumstance. Not only does it deliver the best functional solution (even if you have to pay for it), but it also preserves the device warranty and ensures that no harm will be done to the unit. A similar DICOM-compatibility problem can arise with an old imaging unit. As you will see in Chap. 4, the DICOM standard has been around for a long time and has evolved considerably. As DICOM devices age, they need to be updated with current DICOM software from their manufacturers. In general, this is the same approach I just described with one new catch: the original device manufacturers might not exist anymore. If this is the situation, you will more than likely have one of the following choices: 1. Your original DICOM unit manufacturer was purchased by another, which will help you with the unit support. 2. Your original DICOM unit manufacturer went out of business. In this case, the unit support may have been transferred to another DICOM provider. Many discontinued DICOM units from out-of-business companies are still sold refurbished and supported by other manufacturers. Rule of thumb: digital medical devices that are more than 10 years old should be replaced. In addition to advances in the DICOM standard, the entire digital medicine technology has made great strides. After a certain age, older units not only start looking primitive, they also lack many features and functions – some of which are standard with current technology. When this becomes the case, do not keep patching these dinosaurs; you will do much better replacing them with contemporary models.

3.3

In the Middle of Nowhere

There remains one more common case of providing DICOM functionality: medical devices that were never meant to support the DICOM standard. Despite the rapid progress in digital medicine technology, and the growing DICOMization of the medical imaging workflow, DICOM-free units are still numerous and can come from several sources: 1. Digital medical devices manufactured with no DICOM interface: film scanners, digital microscopes. 2. Generic nonmedical devices (digital or analog). 3. Analog medical devices with no digital output: analog X-rays and more. 4. Old, pre-DICOM medical devices: old CTs, MRs, etc.

3.3 In the Middle of Nowhere

15

The last case of pre-DICOM units is the least interesting and will be discussed in the following chapter on DICOM history. In general, if your device is older than DICOM (which is quite old in itself) you have a no-brainer: replace the device. The first case on our list is much more typical: contemporary digital medical imaging devices (such as many popular film digitizers) that were never meant to have a DICOM interface. This usually happens when device manufacturers want to keep their distance from the medical imaging domain; either because the manufacturer works in other areas/markets, or because it considers medical imaging too complex and troublesome for a stronger commitment. For example, if a CT scanner must be DICOM-compliant, a simple flatbed scanner used elsewhere might not. The problem with digital non-DICOM devices is bigger than their lack of DICOM output. They simply do not fit well into the clinical workflow. For example, each DICOM image must contain patient name and ID tags – indispensably important for accurate image routing and identification. If you have a film digitizer that scans films into plain, non-DICOM digital image formats (such as bitmaps), tagging them with patient or study information is simply impossible. Non-DICOM image formats cannot support DICOM tags. Diagnostic image quality (excellent in DICOM and greatly varying in conventional image formats) is another issue. While DICOM modalities store all clinical data automatically in DICOM image tags, their lessadvanced and more generic non-DICOM counterparts require human assistance, which inevitably leads to data entry errors and lost time. Creative Thinking

One high-profile hospital, where I was making rare consulting appearances, was particularly famous for two things: a lack of PACS and an abundance of clinical IT personnel. The latter kept claiming that one day they will develop their own digital system. After many years of flowcharts and spreadsheets, they indeed came up with their own “PACS solution” – a $100 scanner. The scanner – a bold answer to the overcharging PACS industry – was plugged into the hospital network, and contained the following set of instructions: 1. Keep printing on film 2. Scan film into the system using the scanner 3. Saved scanned files on the computer 4. Name scanned files with patient name and IDs – something like JohnSmith_ID12345.bmp Please do not do this. Just do not. Please.

With all this in mind, if you still want to buy a digital medical device with no DICOM interface, at least take the following precautions: 1. Try to buy from a DICOM manufacturer, which might resell the device with its own DICOM interface. 2. Do not make a non-DICOM device a centerpiece of your clinical workflow. The same logic applies to using generic, nonmedical devices (digital or not). For example, many dermatologists would like to use off-the-shelf digital cameras to

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Where Do You Get DICOM from?

record images of their patients’ skin patterns, infections, and other conditions (which makes perfect sense clinically). Clearly, off-the-shelf digital cameras have no DICOM functionality whatsoever, and likely never will. Therefore, if you want to incorporate those images into your PACS or medical imaging workflow in general, you face the same problem of DICOM conversion and image identification. Because those devices were never meant to be medical, your chances of getting any DICOM assistance from their manufacturers are beyond slim. Instead, look for image importing options in your existing DICOM/PACS software. Many current DICOM workstations and file viewers offer various options for DICOM import, enabling you to convert a plain, conventional digital image (bitmap, JPEG, and such) into a standard DICOM file. Once again, you will have to manually enter the missing information (patient name, ID, study date, and so on), but you will be rewarded with the ability to include this image into your clinical all-digital cycle. In a more general sense, the same approach works for any non-DICOM data, wherever it might come from. Finally, many medical devices are still analog and could stay analog for some time (for example, nonimaging units such as ECG, or even certain modalities such as ultrasound are sometimes not equipped with digital interfaces.) As we know, a device must be digital to be DICOM. Therefore, analog images must be digitized: still images can be converted into digital format and video can be broken into digital image frames. The task of digitization and DICOM conversion is commonly performed with DICOM converters – small box-like devices capable of converting analog images, video, and cine loops into digital, DICOM-compliant images. Moreover, DICOM converters can often interface with standard hospital information systems (to obtain patient data instead of relying on error-prone manual entry) and can send converted images to PACS and other DICOM devices, thus supporting DICOM networking in addition to plain image conversion. DICOM converters are primarily marketed for legacy analog imaging systems, but will do the job with any analog imaging device in general. Another good thing about them, some DICOM converters are actually smart enough to work with certain proprietary imaging formats from the main medical producers and can convert those formats to work with DICOM as well. In addition to DICOM converters, more and more current DICOM/ PACS products offer advanced image export options, accommodating at least digital (but non-DICOM-compliant) images. To conclude, it is still very possible to create a DICOM workflow from generic, somewhat crude, and non-DICOM-compliant imaging devices. Nevertheless, as I have mentioned, image quality and bookkeeping can be problematic. And the need for human interaction makes such solutions slower and more error-prone. Therefore, making DICOM out of nothing is more appropriate for low-volume, marginal imaging solutions, or as a temporary solution in the midst of your transition from a legacy system to a contemporary PACS. However, steady, industrial medical imaging workflow definitely needs to be based on 100% DICOM-compliant devices.

Part II DICOM and Clinical Data

Whatever you need to accomplish in a medical workflow, you will face one of two principal tasks: collecting clinical data or processing clinical data. DICOM was designed to assist you with both in a consistent, clinically-sound manner. To eliminate ambiguity in data interpretation, the standard uses a formal set of rules for representing and encoding data. This part of the book introduces you to these rules. In many ways, this might sound like an introduction to a foreign language spoken by bulb-eyed robots from planet DICOM. This language might not sound pretty to the human ear, but it definitely serves the purpose of making many DICOM conversations easier. Its longevity has become the best proof of its value: despite all the Sturm und Drang changes that the medical and computer industries have experienced since the inception of DICOM in the mid-1980s, it is still spoken and – with occasional updates to the vocabulary – will be spoken for many years to come. Knowing the basics of DICOM lingo is indispensable for any human venturing into the DICOM world – the bulb-eyed robots may get moody. As with any language, DICOM learning begins with an introduction to its dictionary and its grammar.

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4

Brief History of DICOM

Every solution breeds new problems – Murphy’s Law

Because the DICOM standard is some 30 years old, its history has become an integral part of its being; and knowing DICOM’s past can help answer many current questions. Moreover, despite having undergone frequent revisions, the standard has never truly been revolutionized. It has continually evolved, adjusting itself to current practices, yet preserving many original historical features. The natural process of DICOM device manufacturing, selling, upgrading, and using spans many years1; modalities are expensive, and hospital administrators are typically conservative and budget-conscious, trying to get the most out of equipment – sometimes even to the point of keeping things until they fall apart. Should I also mention all discontinued, refurbished, and simply old units that many practices still purchase as the most affordable alternatives? This creates an environment in which drastic updates are not really welcome, and compatibility with older equipment (and, consequently, older DICOM) becomes a must.

How Old Is Your Computer?

If you still believe in quick technology upgrades, consider Table 4.1. It shows web site attendance statistics, collected for a private DICOM vendor in 2011 – thus representing a fairly objective sample of users interested in DICOM products. Who would expect Windows XP (released in 2001) to be on top? And in this age of mobile gadgets, who would imagine Windows NT 4.0 (released in 1996) would make a stunningly triumphant run over Android and Mobile?

1

According to the 2011 CapSite report on PACS replacement (www.capsite.com), 52% of PACS in US hospitals are more than 5 years old – quite an age in this fast-changing field.

O.S. Pianykh, Digital Imaging and Communications in Medicine (DICOM ), DOI 10.1007/978-3-642-10850-1_4, © Springer-Verlag Berlin Heidelberg 2012

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Brief History of DICOM

While manufacturers take time improving their products, users take even longer to embrace these improvements. The result: manufacturers forcing their users to upgrade. Let’s look at a good example in the common world: the Microsoft Internet Explorer 6 countdown project (ie6countdown.com), proactively suggesting that its users abandon the old IE6 browser for much newer versions. Looking at a better example from my own experience: when we informed one PACS company that we were still running an old version of their software, their hilarious response about their own product was: “Are you still using this old crap?” We laughed: only a year before the same guys sold us the “crap” as “a new revolutionary product.” Viva la revolution!

Table 4.1 DICOM web site users by their operating system Operating system Percentage Windows XP 49.04 Windows Vista 17.17 OS X 15.79 Windows 7 12.67 Linux 2.66 Windows NT 4.0 0.73 Windows 2000 0.64 Windows 98 0.64 Mobile 0.28 Windows 2003 0.18 Android 0.09 Windows 32 0.09

The standard itself adds another layer of complexity – even the pure task of DICOM maintenance requires Herculean efforts, due to many tiny, crisscrossed details and plain subjectivities involved in resolving most of them. It is not easy to keep all DICOM vendors, users, and workgroups equally happy – in fact, it is just impossible. As David Clunie pointed out in his DICOM blog (www.dclunie.com), “If we ever had the chance to start DICOM all over again and ‘do it right,’ I am sure that despite our best intentions we would still manage to screw it up in equally egregious ways.” So despite the best intentions, do not be surprised to trip over DICOM’s “egregious ways” in your own practice. In brief, if you work in the current, multifaceted clinical environment you will have to work with multi-generation equipment (DICOM-compliant and not), and you are bound to make occasional archeological discoveries, and find yourself dealing with many layers of DICOM history.

4.1

How Did This All Get Started?

21

Data = [data element 1] + [data element 2] + … + [data element N] Example: Patient = [Name] + [Age] + [Weight] +…+ [Sex] Fig. 4.1 Breaking data into data elements

4.1

How Did This All Get Started?

The standard was conceived in 1983 by a joint committee formed by the American College of Radiology (ACR), and the National Electrical Manufacturers Association (NEMA).2 The primary goal was to develop a standard that would make digital medical imaging independent of particular device manufacturers, thus facilitating the expansion of digital imaging and PACS. If we look at a number of other industries currently struggling with compliance issues, we should admire the foresight of those who reflected upon the structure of digital medical applications long before the spread of contemporary computers and networks. The joint committee – named ACR-NEMA Digital Imaging and Communications Standards Committee – began its work by reviewing many other standards established at that time. Although the committee found nothing specifically fitting for its needs (Horill), it did glean a few valuable hints from the study. The American Association of Physicists in Medicine (AAPM) had recently adopted a standard for recording images on magnetic tape. AAPM took the approach of encoding all information as sequences of data elements; each element could have a variable length (size) and was identified by its unique name (tag). The idea of representing the data as a sequence of tagged data elements was adopted by the ACR-NEMA group. If you have any experience with HTML, or better yet XML, you should immediately recognize the same approach in those very current and popular standards. The concept of using data elements as small building blocks to represent data of any complexity has proven to be extremely useful and robust (Fig. 4.1). The first version of the standard – called ACR-NEMA 300-1985 or ACR-NEMA 1.0 – was published in 1985 and distributed at the Radiological Society of North America (RSNA) annual meeting. Officially, the original ACR-NEMA standard was proposed as a guideline and NEMA did not assume any responsibility for its

2

Part PS3.1 of DICOM standard includes a brief historical overview of DICOM.

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Brief History of DICOM

enforcement or interpretation.3 But the objectives for standardization were well set and well needed. Compliance with the standard has become the de facto imperative for the medical community. As with any first version, ACR-NEMA 1.0 contained errors and imperfections. It was soon realized that the standard required further work with continuous effort and better structure. For these reasons, ACR-NEMA embraced the idea of Working Groups (WG), which are separate subcommittees dedicated to improving specific parts of the growing standard. The first WG VI (currently known as WG-06, Base Standard) was created to work on improving ACR-NEMA 1.0. The result of this work was the second revised version – ACR-NEMA 2.0 (or ACR-NEMA 3001988) – released in 1988. The revised version was sturdy enough to be adopted by the medical device manufacturers, and slowly but surely it started to work its way into medical device interfaces. Even today, you can still find an old CT scanner or digital archive running ACR-NEMA 2.0. The basic compatibility with the current DICOM standard still keeps ACR-NEMA 2.0 afloat, no matter how obsolete it has become. ACR-NEMA 2.0 could have ruled the medical world for much longer had it not been for computer networks. ACR-NEMA 2.0’s ability to communicate medical data between devices was extremely limited. For example, a user could send an image to a remote device, but the standard did not specify what the device should do with the image. Such functional gaps, along with the emergence and rapid spread of networking technology in the late 1980s, demanded more than a simple standard patchwork; they called for another major revision. Another issue mandating a major revision was the need to accommodate the increasing variety of digital devices and their communications protocols. Not only did these devices need a new and more abstract way of looking at the digital information workflow, they also required a solid information model for digital medicine. In response to these changing needs, a third version of the ACR-NEMA standard was created and showcased at RSNA in 1992 in its most basic, prototypical form. The following year was spent in monthly Working Group meetings. The first nine parts of the new ACR-NEMA standard were completed by September 1993 and presented at RSNA 1993 in a much more functional form. The revamped standard was called ACR-NEMA DICOM (Digital Imaging in COmmunications and Medicine) or, because it followed the first two ACR-NEMA editions, DICOM 3.0. Thus the standard became DICOM 3.0 (even though it had no DICOM 2.0 or DICOM 1.0 predecessors). For this same reason the number 3.0 is often omitted and the standard is commonly referred to as DICOM.

3

As noted in the current edition of the standard: “NEMA has no power, nor does it undertake to police or enforce compliance with the contents of this document. NEMA does not certify, test, or inspect products, designs, or installations for safety or health purposes.”

4.1

How Did This All Get Started?

23

ACR-NEMA Versus DICOM

A couple of years ago, I participated in a PACS project, connecting a GE 1.3 Advantage workstation (circa 1989) to a central imaging server. Due to its venerable age, the workstation was not DICOM 3.0-compatible. It was running the prehistoric ACR-NEMA 2.0 standard. However, with minimal tweaking in our DICOM 3.0 software, we were able to connect and push images from the GE workstation to the DICOM 3.0 server. We considered ourselves extremely lucky, but our experiment, in fact, proved a very important point: DICOM 3.0 inherited sufficient structure from its earlier ACR-NEMA predecessors to make such projects possible; and having a pre-DICOM 3.0 unit does not necessarily exclude you from organizing a DICOM 3.0 workflow.

In some sense, having an old ACR-NEMA 2.0 unit would be better than working with a nonmedical digital device as we discussed earlier. Moreover, some manufacturers might still offer you software upgrades and patches to update older ACR-NEMA units to DICOM 3.0 (for a fee, certainly). Nevertheless, do not mistake any of this as advice to stretch your budget by buying some 15-year-old used MR unit instead of a new one. You could very well end up spending your expected and ill-perceived savings on updating the unit and making it compatible with the rest of your PACS. Time Capsule

Once upon a time, I was asked to assist with DICOM connectivity to an old MR scanner from a major PACS company. As always, the task was simple: the scanner had to be set up to connect to a new DICOM server that we were providing. After a long search, the MR owner had located the current MR support team, and we agreed to meet with their field engineer at 9 AM to do an easy 5-minute job of MR configuration update. The engineer came on time, struggled with the MR for a couple of hours, and gave up in desperation. He had been hired and trained several years after the unit was discontinued. So he called another guru, and the story repeated itself. After another hour of phone calls, a third support person was found – one who had been in the business long enough to remember how to log into the MR configuration screen. After that, with a little brainstorming, the configuration was finally done. The configuration itself actually took only five minutes, but the chase resulted in a very long day that left us all mentally and physically exhausted. The Moral of the Story: Beware of old equipment! In many cases, people who still know how to deal with it are likely to be much more rare and valuable (and possibly quite expensive) than the equipment itself.

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Brief History of DICOM

DICOM 3.0 has never been replaced by a DICOM 4.0 and such. Instead, the same 3.0 standard is reviewed annually by the designated DICOM Working Groups, publishing the updated DICOM 3.0 versions and new supplements. The volumes of the standard revisions are numbered as PS3.X-YYYY, where 3 is the standard number, X represents the volume number, and YYYY represents the year of the edition. For example, PS3.5-2011 identifies DICOM 3, part 5, revision 2011 and supersedes the earlier versions of PS3.5. Nevertheless, each new revision still refers to the standard as DICOM 3.0. “Plus ça change, plus c’est la même chose,” as my French colleagues would say. “The more it changes, the more it remains the same.” This naming approach, by the way, can lead to confusion between the revision and the standard numbers. Once I was asked about the software my company was developing: “Do you support DICOM 2003?” Well, there is no DICOM 2003 per se. There was, however, a 2003 edition of DICOM 3.0. Nevertheless, this and similar questions are very common, and should be seen in the same context of DICOM evolution. 1. All DICOM devices you have to deal with are essentially snapshots of DICOM editions used at the time of their development. They can be quite different. 2. DICOM units must work together. Therefore, keeping new DICOM devices backwards-compliant with the previous models is more important than chasing the most recent DICOM features. For this reason, DICOM manufacturers do not really get excited about the most recent DICOM editions. They certainly do not hurry to replace older DICOM protocols with more efficient, recent ones. Even most of the currently produced DICOM units will have the mid-1990s set of functions. 3. If you have to implement a DICOM solution in a complex environment, always choose the most compatible over the most flashy, which is not the same thing. For example, recent DICOM editions have adopted many advanced image compression protocols, allowing equipment to transmit and store medical images much more efficiently. However, in the DICOM world it always takes two to make it work, and transmitting these newly compressed images to an older device simply won’t work when the old devices default to a basic, noncompressed format. This does not mean that you have to stay with the old devices; quite the contrary, you need to seriously consider buying newer units without a doubt! But if you still have a few older ones around, you must make backward compatibility with them your priority. The level of a DICOM unit’s compatibility is reflected in its DICOM Conformance Statement (I told you we’d come back to this). DICOM devices can support many optional and semioptional features, and a wider range of these features is worth thousands of new DICOM additions.

ACR-NEMA 2.0

Although ACR-NEMA 2.0 was officially replaced by the substantially revamped DICOM 3.0 more than a decade ago, some medical practices are still dealing with it. Many DICOM software manufacturers are still preserving

4.1

How Did This All Get Started?

25

ACR-NEMA 2.0 compatibility to be able to accept data in this antiquated format. Conversion of old or proprietary formats to DICOM 3.0 has become a separate business, successfully exploited by DICOM software companies.

All DICOM compatibility questions can be ultimately clarified by referring to the current version of the standard. NEMA maintains the most recent DICOM 3.0 editions on its official DICOM website (http://medical.nema.org) where they can be downloaded or ordered. I think that should about cover things in terms of an introduction. So what do you say to digging a little deeper to find out how the whole thing works? You might want to stretch your bones and your brain for a moment, then buckle your seat belt; the ride can get bumpy.

5

Parlez-vous DICOM?

Each profession talks to itself in its own language, apparently there is no Rosetta Stone – Murphy’s Law

In Chap. 2, we briefly touched on the subject of DICOM data representation. DICOM segments all real-world data into standardized attributes (listed in the DICOM Data Dictionary) and describes any real object as a collection of these attributes known as the Information Object Definition. In this chapter, we look at this process more carefully.

5.1

IT Boot Camp

Because DICOM is all about digital, we should brush up on our computer basics. In our daily lives, we deal with the decimal system: we count by tens, and we have ten digits representing numbers 0–9. Computer data is stored and processed in binary format. Binary, or base-2 system, means that any value will be represented by only two digits: 0 or 1. A bit is a digit in the binary system. Consequently, any bit can take only one of two possible values: you guessed it, 0 or 1. A byte is simply eight bits (8-digit binary number). If you write all possible combinations of eight 0s and 1s, you would get 28 = 256 binary numbers: 00000000, 00000001, 00000010, 00000011, 00000100. . . 11111110, 11111111. In other words, one byte can store values from 0 to 255. All computer hardware (hard drives, RAM memory, flash drives, and so on) stores, reads, and writes binary data in bytes.1 For example, to store 13 bits of data, a computer must allocate a full two bytes (16 bits) of memory. Conventional monitors (and graphics cards) use one byte for each

1

According to Wikipedia, the term “byte” comes from “bite,” as in the smallest amount of data a computer could “bite” at once. Clever, n’est pas?

O.S. Pianykh, Digital Imaging and Communications in Medicine (DICOM ), DOI 10.1007/978-3-642-10850-1_5, © Springer-Verlag Berlin Heidelberg 2012

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Parlez-vous DICOM?

Table 5.1 Multiples of bytes Prefix K M G T P

Name Kilo (KB) Mega (MB) Giga (GB) Tera (TB) Peta (PB)

Binary meaning 210 = 10241 220 = 10242 230 = 10243 240 = 10244 250 = 10245

Metric meaning 103 = 10001 106 = 10002 109 = 10003 1012 = 10004 1015 = 10005

Size difference: binary vs. metric (%) 2.40 4.86 7.37 9.95 12.59

primary color (red, green, or blue) to represent its 256 shades. Consequently, only one byte is available to represent grayscale shades used in radiology. This means that you will get 256 shades of gray on any conventional monitor – you simply cannot squeeze more options into a single available byte. Special radiological monitors and hardware overcome this limitation by allocating more bytes to grayscale shades. One byte also provides enough choices to store all Latin characters (lowercase, uppercase, punctuation signs), so a byte is often viewed as a single-character unit. For example, to store 12 characters, a computer uses 12 bytes of memory; one byte per character.2 Large data volumes, such as images, can require millions of bytes for storage. Therefore, binary system counts bytes in larger numbers: 210 = 1024 bytes correspond to one kilobyte (KB), 1024 kilobytes mean one megabyte (MB), and so on. Table 5.1 summarizes this count. In reality, multiples of bytes are counted in two slightly different ways. Information technology (and DICOM) uses multiples of 210 = 1024, which makes perfect sense from the binary system perspective I just mentioned. Hardware manufacturers use Kilo, Mega, and the others in their metric meaning as multiples of 1000. When they sell you a 1 GB flash drive, it contains 109 = 10003 bytes (metric GB), which according to our table is 7.37% less than 230 = 10243 bytes (binary GB) – certainly makes for a nice commission for hardware sales. When lowercase b is used in Kb, Mb, Gb, it commonly stands for bits, not bytes. Consider networks: network bandwidth (unlike computer storage) is usually measured in kilobits, megabits, and gigabits per second (representing how much data the network can ideally transmit in 1 s). For example, dialup network speed is up to 56 Kbs (kilobits per second), which is identical to 56/8 = 7 kilobytes per second (7 KBs). A standard computer T1 line delivers 1.544 Mbs (megabits per second); DSL delivers on the order of 10 Mbs; and an average PACS network operates in the range of 10 Gbs (gigabits per second). Hexadecimal is a shorthand representation of two consecutive bytes, leading to 65,536 (2562 = 65,536) possible values. Because of their 16-bit (2-byte) nature, hexadecimal numbers can be written using a 16-base numerical system consisting

2

But if you need to work in different languages, you will definitely need more than 256 character options – and multi-language Unicode uses 2-byte letters, just like advance monitors use 2-byte grayscale.

5.2

Text Versus Binary

29

of digits 0–9 and characters A–F representing 10–15 respectively3: “digit” A stands for 10, “digit” B for 11, and so on up to F = 15. This is very similar to our common decimal numbers, except that now we need 16 symbols to represent a hexadecimal digit (so we add A–F for the new digits). We also prefix hexadecimal numbers with 0x to differentiate them from text or decimal numbers. How can you read hexadecimal numbers such as 0x007F? Multiply the digits by the respective powers of 16 ( just like you would with the decimal system using powers of 10). Thus, (ignoring the “0x” prefix) 0x007F in hexadecimal represents: 0 × 163 + 0 × 162 + 7 × 161 + F × 160 = 7 × 16 + 15 × 1 = 127 in decimal. Many current calculators (including the one in Windows) offer binary-decimal-hexadecimal conversion functions, and would have A–F keys for typing the hexadecimal numbers. In a decimal system, where we have 10 digits, 2-digit numbers can take 102 = 100 possible values: from 00, 01, 02, and so on to 99. Similarly, in hexadecimal, with 16 possible digits (0–9 and A–F), 2-digit hexadecimal numbers range from 0x00 to 0xFF, covering 162 = 256 possible values. But this 256-value range, as we already know, corresponds to a byte. For example, hexadecimal 0x7F represents a single byte, and hexadecimal 0x007F represents two bytes: 00 and 7 F. This is why leading zeros are always written in hexadecimal numbers: they do not change the number value (0 x7F = 0x007F = 127), but they show how many bytes of computer storage will be needed to store the number (one byte per two hexadecimal digits). Interchangeably with the 0x prefix, DICOM may also label hexadecimals with an H suffix. For example, number 12 in DICOM would correspond to the decimal 12, while 12 H or 0x12 would mean hexadecimal 12, or 1 × 16 + 2 × 1 = 18 decimal. We will use H and 0x in this book only when necessary: omitting when hexadecimal is implied, and including when ambiguity needs to be avoided. Leading zeros and the A–F digits indicate hexadecimal format as well. As a general rule, remember that nearly all numerical data in DICOM is stored in hexadecimal (binary) format.

5.2

Text Versus Binary

Depending on its format, any data can be stored in either text or binary representations. The text format is typically used for names, dates, IDs, and other text strings. The binary format is used for encoding single numerical values or numerical sequences (image pixels and the like). As we learned in the previous section, binary format has the advantage of storing numbers in a more compact, computer-oriented manner, which makes it a more natural choice for digital data.4 On the other hand, binary data encoding is associated with one serious inconvenience: it depends on the computer hardware. 3 4

Case does not matter, so a–f and A–F are used with the same meaning. However, certain numerical data items routinely read by humans (such as patient weight or size) are stored as text.

30

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Parlez-vous DICOM?

Different computer systems use different byte orders to represent the same multi-byte number. While some systems record the numbers starting from the least significant byte (Little Endian order) others record the same numbers starting from the most significant byte (Big Endian order).5 For example, what a Little Endian computer (such as Windows PC) stores in byte (binary) format as 0x007F, a Big Endian computer (Apple’s Mac) would keep as 0x7F00, reordering the two bytes. When these numbers travel between the systems with different Endian types (for example, when you send a DICOM image from Mac archive to a Windows workstation), their Endian types must be properly converted (byte order reversed); otherwise the numbers will be read backwards, resulting in totally wrong values: 0x007F = 127 and 0x7F00 = 32,512. To avoid data transfer errors, DICOM applications always keep track of their Endian types, and any two connected DICOM units agree on the choice of Endian type during the initial network handshake between them, before they start transmitting any data (we will study this in Sect. 9.4). To make this agreement always possible, DICOM reserves Little Endian as its default byte-ordering type – meaning that all DICOM applications, whatever systems or hardware they are running on, must understand and process Little Endian byte order. The Big Endian/Little Endian computer debate has much more to do with the history of computer hardware than technological merits. If you are involved in any DICOM development, dealing with different Endian types will be one of your responsibilities to ensure cross-platform compatibility of your product. If you are a DICOM user or administrator, bear in mind that byte-ordering problems could occur if you connect devices with different Endian types (PC and Mac, for example). In comparison to numerical values, text data stores each character (byte) independently and therefore always remains in the same order regardless of the hardware. Depending on data type, DICOM uses both text and binary formats. If you open a DICOM file in any word-processing application, you will see a strange mix of somewhat meaningful text strings and totally unreadable symbols – the latter being nothing more than the binary-encoded numerical pieces of DICOM data. The screenshot in Fig. 5.1 shows a fragment of ACR-NEMA 2.0 file, opened in WordPad. As a result, you will always need special DICOM software to read and interpret what is stored in your DICOM data. This differentiates DICOM from later, similarly organized standards (such as HTML or XML), which can be read and modified using any text editor. Now I think it is high time that we see exactly how DICOM text and binary data is formatted.

5

The names Big Endian and Little Endian originate from Jonathan Swift’s Gulliver’s Travels wherein the Blefuscudians’ and the Lilliputians’ are at war over the proper way to open softboiled eggs: on the big end, or on the little end.

5.3

DICOM Grammar: VRs

31

Fig. 5.1 Looking at ACR-NEMA format in WordPad

5.3

DICOM Grammar: VRs

Clinical data comes in a wide variety of formats. Distances can be measured in millimeters, time in seconds, patient names are typically written in alphabetic characters, and so on. So how does DICOM deal with this multitude of formats? Part PS3.5 of the standard defines 27 basic data types – known as value representations (VRs) designed to encapsulate all possible clinical data types. Anything written (encoded) in DICOM must match one of these 27 types. Each VR has its own abbreviated 2-letter name, a definition of what it represents, a description of what characters are allowed in its data, and a prescribed data length. Table 5.2 orders the DICOM VRs by their data formats and complexity. Many DICOM problems and complexities are often rooted in VRs. Please give Table 5.2 a little more attention, and you might want to refer back to it as we discuss a few important issues regarding VRs.

Allowed characters

A string of characters that identifies a device name with leading and trailing spaces being nonsignificant Example: “MyPC01” Person’s name, with a caret character ( ^ ) used as name delimiter Examples: “SMITH^JOHN”,“MorrisonJones^Susan^^^Ph.D, Chief Executive Officer”

A string of characters with leading or Uppercase characters, 0–9, the trailing spaces being nonsignificant SPACE character, and the underscore character ( _ ) Example: “CD123_4” A short character string Example – telephone numbers, IDs A character string that may be padded with leading and/or trailing spaces Example: “Introduction to DICOM” A character string that may contain one or more paragraphs A character string that may contain one or more paragraphs – same as LO, but can be much longer A character string that may contain one or more paragraphs, similar to LT

Definition of VR contents

64 maximum

16 maximum

4294967294 maximum (232-2)

10240 maximum

1024 maximum

64 maximum

16 maximum

16 maximum

Length of data (value) in characters

5

PN, Person Name

Naming devices, people, and instances AE, Application Entity

UT, Unlimited Text

LT, Long Text

ST, Short Text

LO, Long String

SH, Short String

VR name, abbreviated and full Text CS, Code String

Table 5.2 VR definitions

32 Parlez-vous DICOM?

TM, Time

Date and time DA, Date

UI, Unique Identifier (UID)

A string of characters of the format 0–9 YYYYMMDD; where YYYY shall contain year, MM shall contain the month, and DD shall contain the day Example: “20050822” would represent August 22, 2005 A string of characters of the format 0–9, and period (. ) HHMMSS.FFFFFF; where HH contains hours (range “00”–“23”), MM contains minutes (range “00”–“59”), SS contains seconds (range “00”–“59”), and FFFFFF contains a fractional part of a second as small as 1 millionth of a second Example: “183200.00” stands for 6:32 PM

A character string containing a UID that is 0–9, and period (. ) used to uniquely identify a wide variety of items Example: “1.2.840.10008.1.1”

16 maximum

8

64 maximum

(continued)

5.3 DICOM Grammar: VRs 33

Numbers in text format IS, Integer String

AS, Age String

VR name, abbreviated and full DT, Date Time

Table 5.2 (continued)

A string of characters representing an integer Example: “-1234567”

Definition of VR contents Concatenated date-time string in the format: YYYYMMDDHHMMSS. FFFFFF&ZZXX. The components of this string, from left to right, are YYYY = Year, MM = Month, DD = Day, HH = Hour, MM = Minute, SS = Second, FFFFFF = Fractional Second; &ZZXX is an optional suffix for offset from Coordinated Universal Time (UTC), where & is either “+” or “−”, and ZZ = Hours and XX = Minutes of offset Example: “20050812183000.00” stands for 6:30 PM, August 12, 2005 A string of characters with one of the following formats: nnnD, nnnW, nnnM, nnnY; where nnn contains the number of days for D, weeks for W, months for M, or years for Y Example: “018 M” would represent an age of 18 months 0–9, plus (+), minus (−)

0–9, D, W,M, Y

12 maximum

4

Length of data (value) in Allowed characters characters 0–9, plus (+), minus (−) and period 26 maximum (. )

34 5 Parlez-vous DICOM?

A string of characters representing either 0–9, plus (+), minus (−) E, e, and a fixed point number or a floating point period (. ) number Example: “12345.67”, “-5.0e3” Numbers in binary format (same as numbers in text format, but stored in binary) SS, Signed Short Signed binary integer 16 bits long US, Unsigned Short Unsigned binary integer 16 bits long SL, Signed Long Signed binary integer UL, Unsigned Long Unsigned binary integer 32 bits long AT, Attribute Tag Ordered pair of 16-bit (2-byte) unsigned integers that is the value of a Data Element Tag FL, Floating Point Single Single precision binary floating point number FD, Floating Point Double Double precision binary floating point number OB, Other Byte String A string of bytes (“other” means not defined in any other VR) OW, Other Word String A string of 16-bit (2-byte) words OF, Other Float String A string of 32-bit (4-byte) floating point words Other SQ, Sequence of Items Sequence of items UN, Unknown A string of bytes where the encoding of the contents is unknown

DS, Decimal String

8

4

2 2 4 4 4

16 maximum

5.3 DICOM Grammar: VRs 35

36

5.3.1

5

Parlez-vous DICOM?

VR Length

VRs, as we all realize, define DICOM data types; and data size (VR length) is a very important part of this definition. DICOM keeps track of all data sizes in two ways. First, as we will soon see in Sect. 5.5.1, DICOM always records data sizes along with data values – this is how DICOM knows where each data element starts and ends. Second, for some VRs the data length is either fixed or limited – as you can see in the last column of our VR Table 5.2. Data length limits, imposed by VR definitions, are sometimes overlooked in medical imaging software, which easily leads to incompatibility between programs that are “more or less DICOM-compliant.” If you are in DICOM development, make sure that your product is DICOM-compliant enough to format all VR data with correct sizes, and is smart enough to understand improperly sized VRs when they come from another DICOM application. Another reason for DICOM software developers to watch the VR lengths is related to the binary (numerical) VRs. Just like in the case of Big Endian and Little Endian, different computer systems use different sizes for the basic numerical data types (integers, floating point numbers, and such). Thus, the VR size specifications in DICOM also shield DICOM data from the differences in computer hardware and software design. Even length is another fundamental rule of DICOM data encoding: whether fixed-length or not, all DICOM data elements are supposed to have even lengths; that is, they must contain an even number of characters (if text) or bytes (if binary). To ensure this, DICOM adds a single blank space character to any odd-sized text string (such as ST), and a blank NULL byte to any odd-sized binary string (such as OB6) to make their lengths even. For example, the name “Smith^Joe” will always be internally stored in DICOM as “Smith^Joe” with a trailing space added and length set to 10. In one respect, even-length padding of DICOM data can be viewed as an advantage. One can always use it as a parity check to verify the validity of data strings and to mark anything odd-sized as corrupted. However, this approach is rather archaic and comes with the price of junk trailing spaces that any DICOM software should know to add and to trim. Moreover, because many DICOM applications are often used in conjunction with other software (PACS databases, radiology information systems, and such), the issue of ignoring and trimming the trailing blanks permeates the entire workflow chain. The safest approach is to trim DICOM data strings as soon as DICOM data is decoded to be sent to a non-DICOM application. When it is overlooked, what was stored in DICOM as “Smith^Joe” is retrieved as “Smith^Joe”. This might look the same to many humans, but it can easily be misinterpreted as another name (or even another patient) by software that is not aware of the DICOM even-length padding.

6

NULL byte padding is also used for the UI strings, although they are in text format.

5.3

DICOM Grammar: VRs

5.3.2

37

Characters: Foreign and Wild

It is very natural to assume that users in different countries would prefer DICOM data in their native languages, and DICOM does in fact support localization. The language chosen (part of the system localization) also affects the choice of the letters, or characters, allowed in the VR data types. DICOM calls this selection “character repertoire.” Most current DICOM devices use the Latin alphabet (corresponding to the default DICOM character repertoire, labeled as ISO IR-6). It was the first repertoire used in the standard historically and it is quite adequate for many foreign languages. When it is not, transliteration is usually used as a simple shortcut for implementing non-Latin character sets. Instead of changing the Latin set of DICOM characters, foreign characters are mapped into the Latin set in some additional software patch based on their phonetic similarities. This approach is generic and allows users to deal with language-related problems whether they arise from the DICOM standard itself, or from somewhere else. Transliteration might be abandoned as more countries accept regulations mandating the use of their native languages in clinical practices. However, this must be backed by all DICOM manufacturers, whose current support for language localization is far from perfect (as we will see later in Sect. 16.2).

Interface Localization

There was a story about an international research satellite lost in space because what one participating country wrote in millimeters, another country read in inches. Keep in mind that DICOM uses standard metric units: millimeters for sizes, kilograms for weights, and so on. These units are specified in the DICOM standard and should be displayed in any good DICOM interface to avoid ambiguity. DICOM date format has become the most common stumbling block in medical interfaces. The format used by the standard is YYYYMMDD – that is, “20110201” means “February 1, 2011.” However, what is optimal for internal storage might not be optimal for display. Without a doubt, “February 1, 2011” works much better in any DICOM user interface than “20110201” or even “2011.02.01”. When this is not done, or done improperly, the date could be interpreted differently by users from different countries. For example, I have seen 01.02.2011 in DICOM interfaces manufactured for US users, who would rather write this date as 02/01/2011.

In addition to various languages and formats, certain characters in DICOM have reserved meanings and should be used accordingly. In particular, DICOM allows for

38

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wildcard matches in text strings. The asterisk (*) wildcard represents any character sequence; the question mark (?) wildcard represents any single character; and the backslash wildcard (\) represents or. For example, if DICOM needs to search for either CT or MR studies it will search by a modality string CT\MR meaning “CT or MR” modality. In general, wildcards exist to your advantage. If you do not remember the complete patient name, you may enter the few first characters followed by an asterisk (*) to retrieve all similar matches. So entering “Smit*” in virtually any DICOM software should match Smith, Smithson, Smithsonian, and so on. Nevertheless, wildcards can be confusing and lead to DICOM errors as well. DICOM software users can accidentally type question marks in DICOM reports, or include the backslash in a file name that later will be stored in a DICOM data element. As you might guess, such reports and file names can be easily misinterpreted as wildcard searches, resulting in incorrect or lost information.

5.3.3

Text VRs: CS, SH, LO, ST, LT, UT

Text VRs are easy: they are meant to store text strings. They are also the leastdemanding data types, requiring minimal processing (except for trimming those pesky blank spaces at the end when exported to the other applications). The only important thing about the text VRs is that you know their size limits. For example, if CS can hold up to 16 characters, then going even a single character beyond this limit could cause a minor software error or a major PACS problem. Obviously, any length checks should be done by the DICOM software, and all strings exceeding their limits should be either trimmed or converted to longer VR types.

5.3.4

Dates and Times: DA, TM, DT, AS

DA, TM, and DT types are self-explanatory as well: they store dates and times in text (string) format. The most important thing about them is to know the right order of the date/time components. Also, older versions of DICOM and ACR-NEMA used slightly different date and time formats, with period (.) and colon (:) delimiters – for example, writing a time string as 18:32:00, instead of the current 183200. Good DICOM system developers should take this into account – backwards compatibility is very important. Another problem with DA and TM types is that single attributes that really need to be DT types are often broken into DA and TM attributes. Take the Patient’s Birth Date attribute for example, which is assigned the DA type and is complemented by the Patient’s Birth Time attribute (TM type). It would make so much more sense just to merge the two attributes in one of DT type, storing the patient’s birth date and time in a single attribute. Breaking a single piece of information across several attributes creates the problem of keeping the attributes in sync – when one is changed, the other(s) has to be updated as well.

5.3

DICOM Grammar: VRs

39

Since 2008, DT format provides support for time zone information in its &ZZXX component – just in case you were thinking about using it. For example, the &ZZXX offset for United States Eastern Standard Time would be represented as −0500, for Japan Standard Time as +0900. Old “zoneless” timing was fine for local-area systems from the 1990s, when everything was happening on a couple of modalities in the same hospital. Now, as DICOM-based teleradiology starts spanning across multiple countries and continents, and because patients are less hesitant to travel, DICOM had to become more careful about handling time zone information. The next “DT challenge” would be providing a mechanism for synchronizing the time on different DICOM units – that is, keeping them in sync with each other and the universal world time. If you ask me why DICOM needs the AS type, I do not really know. Out of the more than 2000 current standard DICOM attributes, only one (Patient’s Age) uses it. However, the much more popular Patient’s Birth Date attribute (with DA type) is definitely enough to find out the patient’s age – and this is what all PACS rely on.

5.3.5

Numbers in Text Format: IS, DS

IS and DS data types store numerical values (integers and floating point) as text strings. Although less appropriate for computer storage compared to binary, textformatted numbers are used in DICOM quite frequently. First of all, text-formatted numbers do not depend on Big/Little Endian byte order. Second, they are just easy to read – you can display them “as is” in any interface. For this reason, text-formatted numbers are commonly used for the data recorded from the real world, entered and interpreted by humans. Recording a patient’s height or weight is a good example.

5.3.6

Numbers in Binary Format: SS, US, SL, UL, FL, FD, OB, OW, OF, AT

Essentially, these are the same numbers as IS and DS text types, but stored in binary format. SS, US, SL, UL, FL, FD are used to represent single numbers (sometimes a few numbers of the same type are concatenated together). OB, OW, OF are used for long numerical strings – think about storing a sequence of image pixels. In this case, each number in the sequence will have the same byte size (1, 2, or 4 bytes, respectively), and they all will be concatenated into a long binary series. Note that while OB uses one byte per number, OW and OF use 2 and 4. Therefore, OW and OF will be affected by Big/Little Endian byte ordering. Finally, AT stores a pair of 2-byte numbers. This data type corresponds to (Group, Element) tagging of all DICOM attributes, as we will soon see in Sect. 5.4. Thus,

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AT type, unlike the other number types, is used strictly for enumerating DICOM data attributes.

5.3.7

PN – Storing Person’s Names

The PN VR encodes the entire Person Name. Unfortunately, DICOM uses a single field to hold this value – that is, the entire name (first, last, middle, and so on) is recorded in a single PN-type VR. This definitely creates problems when John Smith can be written as “John Smith”, “Smith John”, or even “Smith, John”. To eliminate this uncertainty, DICOM prescribes the following name order, known as “Person Name component group”: FamilyName^GivenName^MiddleName^NamePrefix^NameSuffix All components are separated by the caret (^) character (see PN examples in Table 5.2). However, in a multifaceted medical environment this order is often permuted, resulting in permanently lost information or misidentified patients. There are two remedies to this problem: 1. To identify patients, always use patient IDs (Medical Record Numbers) and not the patients’ names. Using the patient ID leaves little room for spelling errors. 2. When searching for (patient) names on your PACS or any DICOM software in general, use wildcards such as the asterisk (*) meaning any text. As we already know from Sect. 5.3.2, wildcard searches are standard in DICOM. Typing “*Smith*” in the patient name search box will return all patients with “Smith” appearing somewhere in their names, including the names with incorrect component order. In fact, some DICOM programs automatically add wildcards to name searches, to return all similar-looking names. Moreover, certain DICOM applications are smart enough to go beyond wildcard matching when they implement phonetic matching to find the names that sound similar, even with different spelling – for example, matching “Nelson” and “Neilsen.” Features like this are very much appreciated in clinical workflow: they eliminate problems, instead of creating them.7 Recent DICOM editions had to stretch the definition of the person’s name to accommodate a couple of new uses: • Foreign names in ideographic transcription (such as Japanese or Chinese) can be accompanied by Latin phonetic transcription. In this case, different name transcriptions – that is, different component groups – are separated by the equals (=) character: “Wang^XiaoDong = ₺^ዊ᧲”. It is totally unclear how these names can be matched, so DICOM leaves this to software implementations. • You do not have to be homo sapience to become a patient. Veterinary medicine enjoys DICOM in the same ways, so patient names like “ABC Farms^Running on Water” would most likely identify a horse, owned by ABC Farms, and called “Running On Water.” 7

Which is why “sounds like” phonetic matching is implemented in many other software applications, having nothing to do with DICOM – SQL server, for example.

5.3

DICOM Grammar: VRs

5.3.8

41

AE – Naming Application Entities

The AE VR represents a DICOM Application Entity. AE is essentially the name of a DICOM device or program used to uniquely identify it (you cannot have two identical AEs in your PACS network). This makes AE one of the most important VRs for any DICOM network or PACS. Even though DICOM does not have strict requirements for AE naming, Application Entities are typically labeled with numbers and uppercase characters only – no spaces, no punctuation signs, or other characters. In fact, it is not uncommon to see DICOM units that would accept only uppercase alphanumeric AEs. This brings us to the issue of case-sensitivity; simply put, avoid using case-sensitive names. Most DICOM devices will see no difference between Workstation1 and WORKSTATION1, but a few others can get picky. As far as the choice of AE names, it is totally up to you, but the rule of thumb is to use explicit and easy to understand titles, corresponding to the entity’s function (CTARCHIVE) or location (MR1FLOOR). We will look at AEs in more detail in Sect. 7.1.

5.3.9

UI – Unique Identifiers

The UI VR encodes a Unique Identifier primarily used to identify particular instances of DICOM data (objects). While AEs are expected to be locally unique (inside of your network, for example), DICOM unique identifiers (UIs or UIDs) must be globally unique, whenever and wherever used. For example, when you copy a DICOM study from one DICOM unit to another, the second unit should modify the Study UID attribute value to emphasize that it deals with another instance of the same study. Then, changing anything in the second study instance (e.g., reformatting some study images, for example) will not be confused with changes in the original. As Table 5.2 indicates, UI names are built from groups (“components”) of digits, separated by periods. There is a rather bizarre rule in DICOM, often overlooked by standard implementations: “The first digit of each component shall not be zero unless the component is a single digit.” In other words, UI = 1.2.804.114118.2.0909 is illegal (we have a 0 after the period in component 0909), but UI = 1.2.804.114118.2.0 is OK. We talk more about UIDs in Sect. 5.5.8.

5.3.10 SQ – Sequencing Data Sets The SQ VR encodes a SeQuence of data sets, where each set may contain multiple data attributes. This VR provides support for the most complex DICOM structures, allowing VR nesting – placing some VRs inside others. This becomes really useful when you want to group several similar elements into a single data block (and keep them as such) instead of mixing them with other unrelated attributes. In this way, the entire block will be present (or not) and you do not have to worry about the individual block elements.

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DICOM data fragment: data nesting with sequencing Attribute Name Referenced Series Sequence >Series Instance UID >Referenced Instance Sequence >>Referenced SOP Class UID >>Referenced SOP Instance UID and its SQ graph: Series Instance UID Referenced Series Sequence

Referenced SOP Class UID Referenced Instance Sequence Referenced SOP Instance UID

Fig. 5.2 Nesting DICOM data

In DICOM documents (our esteemed Conformance Statements included) sequencing is always indicated with the greater than (>) sign. For example, you could come across a piece of DICOM data looking like the example in Fig. 5.2. In Fig. 5.2 data table, the first “Referenced Series Sequence” attribute is followed by two attributes with the greater than (>) sign, which means that the “Referenced Series Sequence” attribute has an SQ VR, and as a sequence includes the “Series Instance UID” and “Referenced Instance Sequence” attributes. What is even more interesting is that the “Referenced Instance Sequence” attribute is followed by two other elements with double greater than signs (>>), meaning that “Referenced Instance Sequence” in its turn is a sequence as well, and contains two attributes: “Referenced SOP Class UID” and “Referenced SOP Instance UID.” In this way, “Referenced Series Sequence” contains four sub-elements: elements from its own first-level sequence, and elements from its subsequences. Sequencing leads to a complex, nonlinear format of DICOM data when some VRs can branch out from the others producing intricate DICOM data trees – just like our branching in Fig. 5.2. Sequencing does pose some interesting problems for DICOM developers. For example, the VR (data) length for the root element “Referenced Series Sequence” in our example needs to be properly computed to include all nested element data. For this and similar reasons, SQ elements become

5.4

DICOM Data Dictionary

43

harder to implement and to process, and often contain implementation bugs on the software level. If you are designing a DICOM product, handle SQ elements with particular care and use them with moderation. Note that some DICOM data items (such as certain unique identifiers) are meant to be unique, and cannot be used in SQ elements by definition. More details of SQ data encoding will be discussed in Sect. 5.5.5.

5.3.11 UN – Representing Unknown Values The UN VR encodes UNknown values. In many ways, UN value representation is used for anything that does not fit in the other 26 VRs. Most commonly, UN is reserved for manufacturer-specific (proprietary) data, which is not meant to be standard interpretable anyway. I would recommend that anyone working on DICOM application development avoid using the UN VR whenever possible. First, the other 26 VRs sufficiently represent nearly all data types. Second, labeling the attribute type as unknown seems more like an act of despair rather than a structured approach to defining data types. Consider Big/Little Endian byte reordering as one such example. You cannot perform this process for the UN attributes even when it is needed because you just do not know whether the data is binary or text – or for that matter, whether anything needs to be reordered at all. So what did I hope you would learn from all that? Certainly, I do not expect you to have a sponge-like brain and photographic memory to recall every bit of VR grammar. But I do hope that you remember the basics: VRs play the most essential role in DICOM data structuring. They connect DICOM to the outside world. As we conclude our little VR excursion, we now know that VRs are word forms that DICOM understands and speaks. But how do we translate our real-world data items into VR? (Mental drum roll please. Open large velvet curtain.) Enter the DICOM Data Dictionary.

5.4

DICOM Data Dictionary

Part PS3.6 of the DICOM standard contains the complete DICOM Data Dictionary, used to encode all standard DICOM attributes. In addition to the standard dictionary, DICOM vendors can use their own dictionaries for their proprietary data attributes. In either case, the dictionary structure will follow the same rules. We review the rules in this section.

5.4.1

Standard DICOM Data Dictionary

In essence, the DICOM Data Dictionary is the registry of all standard data items (attributes) used in digital medicine. We now know that these items should be formatted into the 27 VR types.

44 Table 5.3 A few lines from the DICOM Data Dictionary Attribute (Group, (data element) Element) tag name Keyword (0008,0001) Length to End LengthToEnd (0008,0005) Specific Character Set SpecificCharacterSet … (0010,0010) Patient Name PatientName (0010,0020) Patient ID PatientID (0010,0021) Issuer of Patient ID IssuerOfPatientID (0010,0030) Patient’s Birth Date PatientBirthDate (0010,0032) Patient’s Birth Time PatientBirthTime (0010,0040) Patient’s Sex PatientSex … (0010,1000) Other Patient IDs OtherPatientIDs (0010,1001) Other Patient Names OtherPatientNames … (FFFE,E00D) Item Delimitation Item ItemDelimitationItem (FFFE,E0DD) Sequence Delimitation SequenceDelimitationItem Item

5

Parlez-vous DICOM?

CS

Retired VM status RET 1-n

PN LO LO DA TM CS

1 1 1 1 1 1

LO PN

1-n 1-n

VR

1 1

To put them in order in the more than 2000-item list, all items are first divided into numbered groups (based on loose similarity of the item contents). Groups are organized by individual elements. Thus, each item is numbered with its own (Group, Element) number, also known as element tag. The tagged elements are also called attributes, or DICOM data elements, or simply DICOM elements. Group and element numbers are hexadecimal – as we saw in Sect. 5.1. Table 5.3 shows an excerpt of the DICOM Data Dictionary. As you can see, the first column contains the hexadecimal (Group, Element) tag. The second “Attribute (data element) name” column – probably the most important to us at this point – explains what real-world data should be stored in this element. Because (Group, Element) tags uniquely correspond to the attribute names, we can refer to a data element by its tag (0010,0010) or attribute name (“Patient Name”) interchangeably. This is a very important DICOM convention – (Group, Element) tags are shorter, with a fixed size and follow a very strict hexadecimal format. All DICOM applications refer to data elements using their (Group, Element) tags and not the descriptive attribute names. The “Keyword” column is a recent addition to the standard. Looking very similar to attribute names, the one-word keywords provide descriptive element tags. These are textual alternatives to (Group, Element) pairs to be used in XML, HTTP (URL), and similar text-based standards, which are increasingly employed in DICOM interfaces. The VR column in the dictionary specifies the format of each data element, as we learned in the previous sections (Fig. 5.3). For example, the “Patient’s Birth Date” element (0010,0030) must be in DA format (that is, as an 8-digit, YYYYMMDD data string).

5.4

DICOM Data Dictionary

45

Real world Data

DICOM Data Dictionary

AS

UN

DA

US

DICOM world

Fig. 5.3 From real world to DICOM world

Data element Value Multiplicity (VM) defines whether the related element may contain only one value of its type, or several. For example, the “Other Patient Names” element (0010,1001) can clearly include more than one name, so its multiplicity is marked as 1-n, where n is any number. How do we put several values into a single element? DICOM concatenates multiple values into a single multi-value value. If these values are binary (have binary VRs), they are simply concatenated. The length of each single-value binary VR is known and fixed (for example, 2 for SS) and this is how they can be read back – in fixed-length pieces. Text values, on the other hand, usually do not have fixed sizes, so they are concatenated using the backslash (\) as the delimiter. For example, if our John Smith patient uses other names such as “Dr Jekyll” and “Mr Hyde”, the (0010,1001) would contain something like “Dr^Jekyll\Mr^Hyde”, corresponding to value multiplicity of n = 2. As we already know, the backslash (\) has a very particular meaning in DICOM, which is “or” in multi-value attributes (see Sect. 5.3.2). For that reason, do not use the backslash (\) for any other purpose (file names, dates, and so on) because it will likely deceive your DICOM software. The RET label in the DICOM Data Dictionary marks retired attributes – those from earlier versions of the standard that will not be supported in future DICOM releases. These elements cannot be redefined and their roles have been delegated to some new, better-designed dictionary elements. The retired items are also italicized. Practically, it takes several years for each new DICOM revision to spread through the industry, so items retired in 2010 will still be used in some DICOM units in 2015 or later. In fact, because all DICOM manufacturers prefer to remain backward compatible with their older models, retired items stay in the dictionary

46

5

Parlez-vous DICOM?

forever (getting less and less use), but are never completely ignored. Good DICOM applications must correctly process these items. Retirement Plans

Updating and retiring DICOM elements may be far trickier than it sounds. I can borrow an example form David Clunie’s blog (www.dclunie.com), discussing some problems with element “retirement plans.” Imagine an old DICOM element – let’s call it XYZdata – that used to be recorded as an integer, but as computer and modality precision progressed, required better decimal precision. Do you just change the attribute’s VR from integer IS to decimal DS in the next DICOM Dictionary release? Not so fast! Think about all existing DICOM units, programmed to work with integer XYZdata values. When they start receiving newly-formatted decimal strings (“123.45” instead of “123”, for instance), they would go nuts. So to keep them happy and sane, you will have to retain the old IS-formatted XYZdata in DICOM Dictionary for a long time, while adding a new XYZdataDouble attribute with proper DS VR. Problem solved? Still too fast! What happens now, if some DICOM unit receives both XYZdata and XYZdataDouble and they happen to be dramatically different, which one should be trusted? You may say “trust XYZdataDouble, it’s the most recent type.” But in fact, the original true data might have come from an older unit where XYZdata was recorded correctly, while XYZdataDouble was messed up later in some buggy integer-to-double conversion. The bottom line: DICOM element retirement can get quite complex and subjective, so you should never ignore retired elements if they are present in your DICOM data. With some respectable knowledge of VRs and the DICOM Data Dictionary at hand, we can finally start speaking DICOM; at least on the very simple attribute level. For example, take a sentence such as: “Patient John Smith, Male, born on August 6, 1954”. Looking at the dictionary excerpt in Table 5.3, we see that there are three data elements in this sentence: “Patient Name” (0010,0010), “Patient’s Sex” (0010,0040), and “Patient’s Birth Date” (0010,0030). These elements have value representations of PN, CS, and DA, respectively. Therefore, replacing names by their tags and applying VR formatting, we can say8: (0010,0010)Smith^John (0010,0030)19540806 (0010,0040)M This is our original sentence, DICOM-encoded. Quite simple, isn’t it?

8

As we will see later, DICOM sorts its data items in increasing (Group, Element) order.

5.4

DICOM Data Dictionary

Table 5.4 Making private data elements (Group, Element) tag Attribute (data element) name (0009,0010) Patient’s Middle Name

5.4.2

47

VR PN

VM 1

Private DICOM Data Dictionaries

I mentioned that the standard DICOM Data Dictionary contains some 2000 entries carefully compiled from the medical imaging industry. You might expect that this leaves a pretty slim chance of encountering an unlisted entry. But what if this happens anyway? In fact, this does happen all the time with various DICOM and PACS manufacturers who need to add their proprietary DICOM attributes into DICOM-encoded data. Let’s say that we designed some DICOM software, and would like to store the patient’s middle name as a separate item. The standard DICOM Data Dictionary does not provide this attribute, so DICOM offers a very simple solution to the problem. All even group numbers are reserved for standard use in the DICOM Data Dictionary. All odd group numbers are reserved for private use. In our “Patient’s Middle Name” case, we can create our own private DICOM Data Dictionary and store an entry that looks like the example in Table 5.4. We would use an odd “proprietary” group number such as 0009 and whatever element number we like. Because our private dictionary is not standard, other DICOM applications would have no idea what our (0009,0010) tag means, but they will know – from the odd group number 0009 – that the tag is private. According to DICOM, unrecognized tags should be ignored; other applications will gracefully skip (0009,0010) when reading our DICOM data. Unfortunately, this is not so simple in reality. Another application may well have its own private dictionary with (0009,0010) being used for some other element (for example, “Physician’s Last Name”). In this case, we run into the classical trap of private tag incompatibility. Our (0009,0010) tag will be accepted by another DICOM provider and will be completely misinterpreted. DICOM makes an effort to avoid this in PS3.5 (Sect. 7.8.1) by reserving certain tags as private creator elements, encoding the implementer of the particular private dictionary; but it always gets trickier in practical life. For that reason, many smart people tried to keep track of at least the most wellknown DICOM providers and their private dictionaries, but this is hard to do. These dictionaries are rarely published and change all the time – that’s what “private” implies anyway. And it is not uncommon to open a DICOM file that contains mostly private data items – private tags are very heavily used; keep this in mind. If you do run into the private element situation one day, consult your DICOM vendor (Table 5.5).

5.4.3

Standard DICOM Command Dictionary

We know by now that all data items (elements) are listed in the standard DICOM Data Dictionary found in part PS3.6 of the standard. But these are data elements.

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Parlez-vous DICOM?

Table 5.5 Example of private Data Dictionary from TeraRecon Aquarius DICOM Conformance Statement (as you can see, odd group 0077 is used to store vendor-specific data elements) Private groups table Module Tag Description VR VM Aquarius Scene Original Series/Study UID (0077, 0010) Private UI 1-N Aquarius Scene Original SOP UID (0077,0012) Private UI 1-N Aquarius Scene Referenced Volume ID (0077, 0014) Private LO 1-N Aquarius Scene Binary Data Name SCS (0077, 0014) Private CS 1 Aquarius Scene Binary Data Name (0077, 0020) Private LO 1-N Aquarius Scene Number of SOP Instance UID Private CS 1-N (0077,0022) …

What if we need to encode commands? DICOM commands such as Print, Store, Move, or Get are used all the time in the medical imaging workflow, but they do not appear anywhere in the Data Dictionary. DICOM encodes commands with the exact same format as data elements, using the reserved 0000 command group. For example, (0000,0100) element is commonly used to represent command type and (0000,0110) to represent command message ID. The DICOM standard does not provide a unified command dictionary (though it would be great to include it in PS3.6). Instead, it explains the use and contents of different command messages in detail in PS3.7 where command message objects are introduced. Annex E in PS3.7 gives the Command Dictionary itself, and we show it in Appendix A.1. Unfortunately, unlike with data elements, DICOM does not support proprietary command attributes. As you will see later in this book, the current set of DICOM commands is rather limited, and was designed for local PACS architecture, which is becoming more and more outdated. Modern digital imaging projects such as teleradiology require more flexible DICOM command structures than the current standard can offer. Allowing proprietary tags in DICOM commands would be the first step in building this flexibility. Meanwhile, this is all we need to know about the command dictionary for now. Chapter 7 provides ample examples of DICOM command objects, explaining commands in more detail.

5.5

DICOM Objects

Do you still remember our simple DICOM translation that we made at the very end of Sect. 5.4.1? Let’s glance at it one more time; it was more than a trivial exercise because there you had just built your first DICOM data object. We used the elements:

5.5

DICOM Objects

49

“Patient John Smith, Male, born on August 6, 1954” and replaced the names with their tags and applied VR formatting to say: (0010,0010)Smith^John (0010,0030)19540806 (0010,0040)M DICOM objects are the most essential part of the standard structure. They are the actual sentences of DICOM lingo that encode, convey, and store DICOM information and commands. All DICOM data (such as medical images, commands, and reports) is always wrapped in DICOM object format. In this format, it travels between various DICOM devices on a DICOM network and gets stored in DICOM files. In fact, even DICOM files can be viewed as memory dumps of DICOM objects to file media. When we studied the DICOM Data Dictionary, we learned that DICOM breaks all real-world data into its building blocks: data elements, encoded with 27 available VR types. A DICOM object is nothing but a collection of data elements. For example, consider a digital medical image. It will have several attributes such as image width, height, colors (palette), date the image was acquired, and so on. All these attributes can be found in the standard DICOM Data Dictionary and will be translated into DICOM data elements, each with its own tag and value. The sequence of these translated elements, which describes the image in its entirety, becomes the image’s DICOM object. DICOM objects, however, can grow much more complex than simple element sequences. When we looked at VRs, I mentioned one particular VR type used for sequencing – SQ. The SQ VR is designed to hold a sequence of data element sets – each set being, in essence, a separate DICOM object. These DICOM objects in turn may also contain SQ VRs, meaning that DICOM objects can contain sets of other DICOM objects. This recursion or nesting of DICOM objects creates a more complex tree-like structure, making DICOM objects look like data trees, with DICOM objects being the branches and data elements the leaves (Fig. 5.4). How does DICOM write all this complex branching data? It uses very basic data encoding rules that we are about to review.

5.5.1

Encoding Data Elements

Encoding in DICOM means writing attribute data in DICOM-specific format, converting potentially complex DICOM attribute values into a sequence of bytes. To write the entire data object one must know how to encode the individual data elements. Part PS3.5 of the DICOM standard defines two major encoding types: implicit VR encoding and explicit VR encoding. Implicit encoding is the simplest one, and is used as the DICOM default. It is defined in Table 5.6. As an example, consider our favorite patient, Joe Smith. The group number for the Patient Name entry in the DICOM Data Dictionary is 0x0010, and the element number is 0x0010. The original value length of the “Smith^Joe” text string is 9, but DICOM

50

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Parlez-vous DICOM?

DICOM Object (root)

Data Element with SQ type

DICOM Object (nested)

Data Element

Data Element

Data Element

DICOM Object (nested)

Data Element with SQ type

DICOM Object (nested)

Data Element

Data Element

Data Element

Fig. 5.4 DICOM object nesting Table 5.6 Implicit VR encoding Tag Group Number Element Number (2-byte unsigned (2-byte unsigned integer) integer) 2 bytes 2 bytes

Value length 4-byte integer L

4 bytes

Value Even number L of bytes containing the data element value L bytes

needs to make it even (VR length must be even, see Sect. 5.3.1), so it adds a trailing space, converting the name string into “Smith^Joe”. The name length now becomes L = 10 = 0x0A characters, and we encode the patient name attribute as shown in Table 5.7. Note one important detail: the default Endian type in DICOM is Little Endian (see Sect. 5.2), meaning that multi-byte numbers are written starting from the lowest byte. Therefore, for Group = 0010, the lowest (rightmost) byte 10 comes first, and the highest byte 00 is the next – the same applies to the Element and Length encoding. The 18 bytes that you see in the Binary line is exactly what DICOM will write as the encoded Joe Smith patient name. To encode multiple data elements (which is always the case), DICOM will simply concatenate their individual encodings into a single binary string – we will see quite complex examples of this in Chap. 7. Because the length of each data item is included in the item’s encoding – and Group, Element, and VR Length fields have a fixed size – you can always break concatenated elements into separate ones. For example, with our implicit Endian encoding in Table 5.7, just read the first 2 + 2 = 4 bytes for the (Group,Element) tag, then read length L from the following four bytes, and then read L bytes for the element value. After this, the next element starts, and the entire process repeats itself.

5.5

DICOM Objects

51

Table 5.7 Implicit encoding example Byte # 1 2 3 4 5 6 7 Decimal 16 0 Binary 10 00

16 0 10 00

10 0A

0 0 00 00

8

9

10

11 12 13 14 15 16 17 18

0 00

S 53

m 6D

i 69

Group Element VR length L = g = 0010 e = 0010 10 = 0x0000000A

t 74

h 68

^ 5E

J o e 4A 6 F 65

(space) 20

VR value = Smith^Joe (with trailing space)

Table 5.8 Explicit VR encoding (except OB, OW, OF, SQ, UT, and UN) Tag VR Value length Value Group Number Element Number VR (2 2-byte Even number L (2-byte unsigned (2-byte unsigned characters) integer L of bytes containing integer) integer) the data element value 2 bytes 2 bytes 2 bytes 2 bytes L bytes

Table 5.9 Explicit encoding example Byte # 1 2 3 4 5 6 7 Decimal 16 0 16 0 P N Binary 10 00 10 00 50 4E g = 0010 e = 0010 VR type

8

9

10 11 12 13 14 15 16 17 18

10 0 S m i t h ^ J o e (space) 0A 00 53 6D 69 74 68 5E 4A 6 F 65 20 VR length L = VR value = Smith^Joe (with trailing space) 10 = 0x000A

Table 5.10 Explicit VR encoding (for OB, OW, OF, SQ, UT, and UN) Tag Group Number (2-byte unsigned integer) 2 bytes

Element Number (2-byte unsigned integer) 2 bytes

VR VR (2 characters) of OB, OW, OF, SQ, UT or UN 2 bytes

Reserved (2 bytes) set to a value of 0000 2 bytes

Value length 4-byte integer L

Value Even number L of bytes containing the data element value

4 bytes

L bytes

Explicit data encoding is very similar to implicit. It has two subtypes. The first is applied to all VRs except OB, OW, OF, SQ, UT or UN – as shown in Table 5.8. As the name suggests, with explicit VR encoding we include 2-character VR types. What used to be a 4-byte VR length field in implicit encoding is split into a 2-byte VR type, and a 2-byte VR length. So the same example with the patient name will be now encoded as in Table 5.9. For VRs with OB, OW, OF, SQ, UT, or UN an explicit encoding will use a slightly different method. Two reserved bytes (set to 0000) follow the VR name and allocate four bytes for the VR length (just like with implicit encoding) as shown in Table 5.10. For example, to encode a pixel buffer for a 256 × 256 image with OB type (one byte per pixel, total of 256 × 256 = 65536 = 0x00010000 bytes), we would have Table 5.11.

52

5

Table 5.11 Explicit encoding example Byte # 1 2 3 4 5 6 7

8

9

Parlez-vous DICOM?

10 11 12 13 14 15 … 65547 65548

Decimal 224 127 16 0 O B 0 0 0 0 1 0 Binary E0 7 F 10 00 4 F 42 00 00 00 00 01 00 g = 7FE0 e = 0010 VR type Reserved VR length L = 0x00010000

0 3 0 … 10 10 00 03 00 … 0A 0A VR value (pixels, one byte per pixel)

One cannot mix explicit and implicit VR encoding in the same DICOM object – one type only must be used consistently. Even though explicit encoding seems somewhat redundant, it has its advantages: 1. Redundancy in VR names helps avoid data decoding errors. 2. As DICOM evolves, some VR types change and might not be the same in the present DICOM Data Dictionary as they used to be. In this case, explicit encoding preserves the original type names and provides additional backwards compatibility. 3. Explicit encoding is important for encoding nonstandard (proprietary, vendorspecific) VRs, which cannot be found in the standard DICOM Data Dictionary. Nonetheless, DICOM defines implicit VR with Little Endian byte ordering as the default data encoding method. Because explicit and implicit VR encoding cannot be mixed, the decision regarding which technique to use must be made at the very beginning of any data transfer – DICOM applications can negotiate and agree on encoding types before they exchange any data. We will see more of this when we study DICOM Transfer Syntaxes in Sect. 9.4.

Explicit-Implicit Conversion

Often DICOM data will have to be converted from implicit to explicit VR representation and back – depending on the type required for current data transfer. Obviously, converting explicit to implicit is easy: just drop the VR types. Going back is trickier: you need to match each attribute tag in the DICOM Data Dictionary to find out its VR type. In some cases though, this wouldn’t work. For example, if the attribute was proprietary (odd-numbered group number) it wouldn’t exist in the standard dictionary. This is probably the only case when the UN (unknown) VR type comes in handy (see Sect. 5.3.11) – it can cover all unidentifiable VRs.

In any event, DICOM keeps the length of data items even – just as we did with encoding Joe Smith’s name. VR length plays another important role in DICOM data reading (decoding) – it helps skip unknown elements. If your DICOM application encounters an element that it cannot understand, it should simply scroll the L = Length bytes and start reading the next element. The ability to gracefully ignore unknown data is quintessential for robust apps – they should not freeze or fail when they run into something they cannot interpret. Just skip to the next!

5.5

DICOM Objects

53

Finally, DICOM offers an option of using undefined length when data of unknown size is surrounded by standard delimiters. It happens mostly in SQ items, so we will consider it a bit later in Sect. 5.5.5.

5.5.2

Encoding Data Groups

We already know that all DICOM elements in the DICOM Data Dictionary are organized into groups and are labeled as (Group, Element) pairs. For example, group 0010 in the Data Dictionary gathers all elements related to a patient (name, ID, weight, size, age, and so on); group 0028 is dedicated to the information about the image (width, height, color depth); and group 7FE0 consists of only one element: pixel data. Groups with odd group numbers are not present in the Data Dictionary because they are reserved for manufacturers to store their proprietary data. When DICOM data elements are encoded into DICOM objects, they are written there strictly in the order of their (Group, Element) tags, starting from the lowest. Thus, elements are sorted in ascending order within each element group, and groups are sorted in ascending group order. For example, element (0008,0012) would be recorded before element (0008,0014). Element (0010,0010) would be written after the first two because it has a higher group number. DICOM Developers: Element Order in DICOM

Knowing that all DICOM elements in a DICOM object have to come in the well-defined ascending order of their element tags plays an important role in DICOM software design. First of all, this is a major validation tool. If, when reading a DICOM object, element (0010,0010) is encountered before element (0008,0012), then something went seriously wrong with the object. Most likely, the data was corrupted beyond recognition and needs to be rejected. Second, most basic data was historically placed into the groups with the lower group numbers, such as patient data in group 0010. This can help a DICOM application avoid reading the entire, potentially large, DICOM objects if it is only looking for a few basic tags. Start reading from the top. As soon as the required group is processed, stop and skip the rest.

Apart from (Group, Element) tag ordering, there is nothing of particular concern about encoding DICOM data groups, with one small exception: group length tag. Although this tag has been retired9 in DICOM starting 2008, it still can be found in

9

Group length was retired in DICOM data objects (gggg > 0000), but still has to be present in DICOM command objects (gggg = 0000), as we will see in Sect. 7.2.2. To justify this exception, DICOM declared that “elements in group 0000 are not standard elements”, which sounds a bit funny for a standard.

54

5

(Group, Element)

VR

Length

Parlez-vous DICOM?

Value

…… (data elements before group 0010)

(0010,0000)

UL

4

L bytes

(0010,0010)

PN

10

Smith^John

(0010,0030)

DA

8

19540806

L data bytes

…… (more group 0010 elements)

(0010,4000)

LT

(0012,0000)

UL

12

No_comments_

…… (remaining data elements)

Fig. 5.5 Example of group length element: element (0010,0000) at the very beginning of group 0010 contains data value equal to L, where L in the total number of bytes in the encoded group 0010 data elements (following right after element (0010,0000)). The number of bytes in (0010,0000) is not included in L

many implementations, and is worth looking at. For each DICOM group gggg, its very first element (gggg, 0000) is reserved to hold the entire length L (in bytes) of all following gggg data elements present in the given DICOM object. Because all data and tag lengths in DICOM are even, L is the even number of bytes from the end of the (gggg,0000) element to the beginning of the next group in this object. When a DICOM object is written, element (gggg, 0000) must be placed in the beginning of each new group gggg in UL VR format, recording the entire length of all group gggg elements up to the beginning of the next group (Fig. 5.5). The reason for group length encoding is exactly the same as it is for element length encoding. If your application does not need to read the group gggg data, it might read only the group length L from the (gggg,0000) tag, and then fast-forward L bytes, proceeding to the next available group. Because the number of elements in gggg can be large, this forwarding can substantially speed up DICOM object reading (a technique known as partial parsing). This becomes particularly handy when dealing with proprietary, odd-numbered groups. Such groups can be correctly interpreted only by their manufacturers and have to be ignored by everyone else. If group length is provided in element (gggg,0000), skipping such groups becomes a very easy exercise. Group length can also be viewed as a basic security feature. Group length (just like data checksums) makes it harder to modify something in part of a DICOM object without destroying the entire object structure.

5.5

DICOM Objects

55

There is no free lunch, however, and this method has its own principal drawbacks. To be able to write the (gggg,0000) tag, DICOM software applications must know the length L of all data elements from the gggg group that are present in the DICOM object. That is, it needs to know the total length of all gggg elements, which will be written after the very first (gggg,0000) tag. This implies that all gggg elements must already be available to you with their final values and encoded ahead of encoding the (gggg,0000) element. To DICOM software developers, this means two things: 1. Writing DICOM objects on-the-fly becomes practically impossible because all data must be collected first. 2. Modifying any data element in DICOM objects (for example, replacing Patient Name with another name) inevitably affects the length of the related element’s group, which also needs to be updated accordingly. This additional processing proved to be too much for those of us living in the lazy world of software developers. Consequently, most DICOM software ignores (gggg,0000) group tags, or worse, writes them incorrectly. This might have been the final straw behind retiring group element tag in post-2007 DICOM: the honorable (gggg,0000) group tag is now destined to fade away, and will eventually disappear from future standard releases. However, DICOM apps still must be able to read group length tags, if they are provided: “All implementations shall be able to parse group length elements, and may discard and not insert or reinsert them… No implementation shall require the presence of group length elements.” As a result, most DICOM software reads (gggg,0000) tags, but makes absolutely no use of them. This certainly works as the best defense against inconsistently written or incorrectly updated group length values.10

5.5.3

Example: Element and Group Lengths

Encoding element lengths is a very important part of DICOM data representation, and deserves an illustration. Let’s consider a simple C-Echo-Request object used in the DICOM standard to verify DICOM network connectivity. I will talk more about C-Echo when we study DICOM services in Sect. 7.2.2, but for now all we need to know is what this object contains. The data encoded in a C-Echo-Request is shown in Fig. 5.6. C-Echo-Request is a DICOM command object, which also means that it contains attributes from group 0000 only (DICOM command attributes) and is encoded with implicit VR encoding(mandatory for all DICOM command objects). As the table in Fig. 5.6 shows, the object contains only five elements from group 0000: elements 0000, 0002, 0100, 0110, and 0800. I did the length math under each element in the table. Element tag (group and element numbers such as (0000,0002)) will always be encoded with 2 + 2 = 4 bytes, and the length of the data field will always be encoded with four bytes.

10

Despite the retirement, references to group length tag can be still found in many parts of DICOM standard – in particular with command objects encoding (gggg = 0000) in PS3.7.

56

5 Element Tag

Value Length

(0000,0000)

4

2+2 bytes

4 bytes

4 bytes

(0000,0002)

18

1.2.840.10008.1.1

2+2 bytes

+4 bytes

+18 bytes

(0000,0100)

2

0030

2+2 bytes

+4 bytes

+2 bytes

(0000,0110)

2

0001

2+2 bytes

+4 bytes

+2 bytes

(0000,0800)

2

0101

2+2 bytes

+4 bytes

+2 bytes

Value

Parlez-vous DICOM?

Element length

56 =12 bytes

=26 bytes

=10 bytes

=10 bytes

=10 bytes

Fig. 5.6 Doing length math for C-Echo-Request object

The total of all four elements following element (0000,0000) is 26 + 10 + 10 + 10 = 56 bytes. Therefore, 56 will be stored as the value of the (0000,0000) element. If there was another group after group 0000, we could have skipped 56 bytes right after the end of the (0000,0000) element and this would have taken us to the next group available.

5.5.4

Encoding DICOM Data Objects

If you, my dear reader, have made it through the data element encoding part, you have really nothing else to fear. DICOM objects are sequences of DICOM data elements (Fig. 5.7), and therefore are encoded in a straightforward element-wise fashion. Sure, a data element may contain an SQ item, which would correspond to a nested DICOM object as we discussed earlier. This adds complexity, but does not change the principle. A single rule determines the element order: all data elements inside DICOM objects must be ordered by their tag (Group, Element) number. This ordering serves at least two practical purposes:

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DICOM Object

Data

Data

Data

Data

Element

Element

Element

Element

Format:

Element tag: Group number

Example:

Element number

(0010,0010)

VR PN

Value Length 10

Value

Smith^John

Fig. 5.7 Object without SQ elements – simple sequence of data elements

1. Helps verify data integrity (similar to what even data length does). If, when reading a DICOM object element after element, you run into an element with a smaller tag number than the one you read before, the object is corrupted. 2. Helps put data in the order in which it should really be interpreted. For example, image width and height have lower tag numbers than the actual image pixel buffer. This means that width and height are read first, and only after will you read the pixels, already knowing how to group them into the width-by-height pixel matrix.

5.5.5

SQ: Encoding DICOM Object Sequences

As we have already seen, one VR type – SQ (sequence) – plays a very special role in DICOM data encoding. It allows us to store entire sequences of DICOM objects in a single SQ VR (see Sect. 5.3.10). With the introduction of the SQ type, DICOM object layout changes dramatically. The SQ element has no data of its own. Instead, it contains a sequence of DICOM objects. These objects become nested in the parent DICOM object (the object containing the SQ element). Moreover, because the DICOM objects in the SQ sequence obey the same DICOM object format, they can also contain SQ elements. As a result, we could have multiple nesting levels as shown in Fig. 5.8. The nesting stops at the level where nested DICOM objects have no more SQ elements. Thus, the entire concept of object sequencing comes quite naturally. If it looks thorny to you, think about the structure of this book. The book has chapters (data

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DICOM Object

Data

SQ Data

Data

Data

Element

Element

Element

Element

DICOM Object

DICOM Object

Level 0

DICOM Object Level 1

Data

Data

SQ Data

Element

Element

Element

DICOM Object

DICOM Object

DICOM Object Level 2

Data

Data

Data

Element

Element

Element

Fig. 5.8 Object with SQ elements – nesting DICOM objects

elements), but some of these first-level chapters have second-level chapters (subsections) inside them, and some second-level chapters have their own subchapters as well (third level), and so on. So, if we consider this book as a large DICOM object, chapters with subchapters would be our SQ elements. In fact, many current data representation languages (XML, for example) make full use of data nesting just like DICOM does. How do we encode SQ elements though? Without SQ everything is easy. A DICOM object is a list of its data elements, so we encode it as a list as well. For SQ elements, DICOM provides a special SQ encoding scheme that encapsulates the DICOM object sequence within a single VR. This is similar to what we learned in Sect. 5.5.1 and depends on the VR encoding method (explicit or implicit) and our choice of data SQ item delimiters (known or unknown length). The following three examples in Table 5.12 cover all possible SQ encoding methods (nested DICOM objects are in gray cells): If you compare the three examples, you might discover the SQ encoding rules on your own. The rules include: 1. DICOM objects in SQ sequence are encoded as sequence items. 2. Each object-item in the SQ sequence is preceded by the (FFFE, E000) tag (known as item delimitation item). This item is followed by one of the following:

Table 5.12 SQ encoding Example 1: SQ data element with explicit VR defined as a sequence of items (VR = SQ) of undefined length, containing two DICOM objects (items) of explicit length Tag VR Value length Value (sequence of DICOM objects) Sequence Delimitation Item (gggg, SQ 0000 FFFFFFFF First Item Second Item eeee) (undefined Tag: Length: Value: Tag Length: Value: Seq. Seq. Delim. length) (FFFE, 0x1234 DICOM (FFFE, 0x1000 DICOM Delim. Item Length E000) object E000) object Tag (FFFE, 00000000 E0DD) 4 bytes 2 bytes 2 bytes 4 bytes 4 bytes 4 bytes 0x1234 4 bytes 4 bytes 0x1000 4 bytes 4 bytes bytes bytes Example 2: SQ data element with implicit VR defined as a sequence of items (VR = SQ) with three items of explicit length Tag Value length Value (sequence of DICOM objects) (gggg, eeee) 0x00000A00 First Item Second Item Tag (FFFE, E000) Item Length: Value: DICOM Tag (FFFE, Item Length: Value: DICOM 0x000004F8 object E000) 0x000004F8 object 4 bytes 4 bytes 4 bytes 4 bytes 0x04F8 bytes 4 bytes 4 bytes 0x04F8 bytes Length math: 0x04 + 0x04 + 0x04F8 + 0x04 + 0x04 + 0x04F8 = 0x0A00 Example 3: SQ data element with implicit VR defined as a sequence of items (VR = SQ) of undefined length, containing two items where one item is of explicit length and the other item is of undefined length Tag Value length Value (sequence of DICOM objects) Sequence Delimitation Item (gggg, FFFFFFFF eeee) (undefined length) First Item Second Item Item Item Value: Item Item Length Value: Item Length Seq. Seq. Delim. Item Tag Length DICOM Tag 0xFFFFFFFF DICOM Delim. 0x00000000 Delim. Tag Length (FFFE, 0x0000 object (FFFE, (undefined object Tag (FFFE, 0x00000000 E000 17B6 E000) length) (FFFE, E0DD) E00D) 4 bytes 4 bytes 4 bytes 4 bytes 0x17B6 4 bytes 4 bytes Undefined 4 bytes 4 bytes 4 bytes 4 bytes bytes length

5.5 DICOM Objects 59

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i. Explicit length of the DICOM item (for example, first and second items in example 1). This length defines the number of bytes to be read to get the following DICOM object. ii. Implicit (undefined) length set to hexadecimal FFFFFFFF (second item in example 3). In this case, we need to mark the end of the DICOM object item with (FFFE, E00D) tag. This delimiter is followed by its zero length because it only marks the end of the DICOM object and has no data of its own. 3. Similar to item 2, the entire SQ sequence can have explicit or undefined length: i. If explicit length is used (example 2, length 00000A00), this length is equal to the total length of the encoded DICOM object sequence (immediately following the length tag). In example 2, we read 00000A00 bytes and start breaking them into DICOM objects based on (FFFE, E000) tags. ii. We could use implicit (undefined) length instead, which in DICOM is marked with FFFFFFFF. Because the length is not known, we have to mark the end of the SQ sequence with the sequence delimitation tag (FFFE, E0DD) followed by its zero length. If you think about this, the undefined length is identical to the XML approach with the (gggg,eeee) SQ tag playing the role of the XML “” marker. Similarly, on the object/ item level (FFFE, E000) works just like the XML “”marker. Apart from this XML analogy, the principal beauty of undefined length delimiters is that you do not have to compute the total length of your DICOM objects or sequences. If you make a tiny error in this rather complex length value (example 2) the entire sequence will become unreadable. Therefore, undefined-length delimiters are more reliable and easier to implement. On the other hand, explicit length (example 2) permits you to skip the entire SQ elements or their individual items if you are not interested in reading them. For example, after you have read the 00000A00 sequence length in the example 2 object, you may fast-forward past 0x00000A00 = 2560 bytes to the next element, ignoring the entire SQ element if, for whatever reason, you do not need those bytes for your processing. In short, you gain performance, which probably was a big deal in 1983, but not so much now. These days, carefully written DICOM software reads the most complex DICOM objects in fractions of a second, and the performance would be much more affected by network speed or image encoding than by the use of implicit length items. Therefore, if you implement DICOM software, I would recommend staying with implicit (undefined) length delimiters; they are simple and straightforward for writing your DICOM objects. However, bear in mind that if you can choose between explicit and delimited lengths while writing your own DICOM sequences, you must implement both length types to read DICOM objects. In other words, write as you like, but be polite to other DICOM manufacturers who might have used the opposite length encoding type. You are still responsible for being able to read their objects. Lastly, keep in mind that each DICOM object item in our three examples could in turn contain SQ items and therefore would be encoded with the same rules as we just discussed. Let’s review the nested object from Sect. 5.3.10 in the way it will be stored, using SQ elements, as shown in Fig. 5.9.

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DICOM data fragment Attribute Name

Tag

Referenced Series Sequence

(0008,1115)

>Series Instance UID

(0020,000E)

>Referenced Instance Sequence

(0008,114A)

>>Referenced SOP Class UID

(0008,1150)

>>Referenced SOP Instance UID

(0008,1155)

its SQ graph: (0020, 000E) (0008, 1115)

(0008, 1150) (0008, 114A) (0008, 1155)

…and its representation with SQ nested encoding: DICOM Object (root) Level 0 SQ Data Element (0008, 1115)

DICOM Object Level 1 Data Element (0020, 000E)

SQ Data Element (0008, 114A)

DICOM Object Level 2 Data Element (0008, 1150)

Data Element (0008, 1155)

Fig. 5.9 Object with SQ elements

In the example shown in Fig. 5.9, each SQ element contains only one DICOM object. Each of our three encoding examples can be applied with different length explicitness and encoding starts from the bottom. First, we encode a DICOM object at level 2 (it does not have SQs and we encode it as a plain VR sequence). Then the encoded object is placed in the (0008, 114A) SQ element with explicit or undefined

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length and we arrive at level 1. Finally, the entire encoding process is repeated to create the level 1 DICOM object and to put it inside (0008,1115); (0008,1115) becomes the only element in the root (parent) DICOM object, ready to be stored or networked. If you are programming SQ encoding, you can easily implement it with recursion, just like you would for any tree-like data structure.

5.5.6

Required and Optional Data Elements

Notice that, despite the flexibility of DICOM objects, you should still be very careful with what you put into them. Storing an ultrasound image along with the Hounsfield grayscale CT calibration in the same DICOM object would hardly make sense. PS3.3 (Information Object Definitions) goes into great detail describing the tags and elements required for each particular DICOM object type, such as particular modality images. This leads to another classification of all data elements: required, conditional, or optional (Sect. 7.4 in PS3.5). To be exact, DICOM defines the following attribute types shown in Table 5.13. For example, patients in a DICOM workflow are identified by their IDs rather than their names. Therefore, the “Patient ID” attribute (0010,0020) would be Type 1 in virtually any DIOM data object. Consequently, the “Patient Name” attribute (0010,0010) can often be Type 2. The “Image Lossy Compression” attribute (0028,2110) is often Type 1C – it must be specified only if the images have undergone lossy image compression. While pure DICOM devices such as digital modalities automatically include all required elements in the objects they produce, be careful when you use lessstandard DICOM conversion software. It is not uncommon to see a film digitizer encapsulate your scanned film into DICOM format and totally forget to enforce the presence of required tags. DICOM objects (including DICOM files) missing required elements will be considered illegal, and could be rejected or totally misinterpreted by other DICOM devices. Take missing Patient ID as an example: if it is not specified, the object becomes invalid. Worse, if it is specified but left blank, it could be interpreted as “any” (blank elements can be interpreted as wildcards). This means that you can scan several different patients on your digitizer and they will all be merged into one single patient! Believe it or not, this is one of the most widespread errors found in medical data archives.11

5.5.7

Storing Image Data

Images follow the same data encoding rules and therefore can easily be put into DICOM objects. As you might imagine, each medical image has several important

11

There are nice public tools for checking DICOM attribute compliance. For example, the DVTk (DICOM Validation Toolkit) is available at http://www.dvtk.org.

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Table 5.13 Data element types Attribute type When to use 1 Such attributes shall be present with an explicit value, and shall be supported by DICOM applications and services 1C Such attributes shall be present with an explicit value provided the specified condition is met. They shall be supported 2 Such attributes shall be present with an explicit value or with a zero-length value if unknown. They shall be supported 2C Such attributes shall be present with an explicit value or with a zero length if unknown, provided the specified condition is met. They shall be supported 3 Such attributes may be present with an explicit value or a zero-length value. They may be supported or ignored

elements that correspond to attributes in the standard DICOM Data Dictionary. A few of these include: • Image height – corresponds to the “Rows” attribute (0028,0010). • Image width – corresponds to the “Columns” attribute (0028,0011). • Image pixel data – corresponds to the “Pixel Data” attribute (7FE0,0010). The latter contains the actual image and in many cases takes some 95% of the DICOM object size (medical images are large). Moreover, you can put sequences of image frames into a single DICOM object, which is how DICOM stores digital video such as ultrasound cine loops. In fact, we can look at this process from a wider perspective: interpreting digital images as digitized signal sequences. This makes them similar to any other signal data – audio, for example – that you might want to record and put into DICOM objects. With sensible use of the Data Dictionary it all becomes very possible and you can easily encode your favorite movie as a single DICOM object with multiframe image and audio.

I Want My MTV!

As I just said, DICOM stores videos as sequences of individual image frames. For example, a single ultrasound cine loop, 10 s long, recorded at 25 frames/s will be digitized in DICOM as 10 × 25 = 250 consecutive image frames stored in a single DICOM object. When these are displayed in DICOM software, they still will be displayed as a series of 250 still images. In other words, you always have to push some “Play” button to make them run as a video. I have seen several people puzzled by this concept: if this cine loop is a video, why does not it play automatically? Simply because the playback is a function of your viewing software and not the function of DICOM. DICOM stores 250 still video frames accompanied by the (0018,0040) “Frames Per Second” attribute value just to indicate that these frames should be played as a video.

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5.5.8

Parlez-vous DICOM?

Unique Identifiers

Whether you ever played with object-oriented applications or not, you should be familiar with the notion of an instance. In brief, an instance is a snapshot of an object in time with all its current data values. Changing an object’s data always produces another instance of the object. Consider a medical image; say the head X-ray of John Smith, stored in a DICOM object. If you make a copy of this DICOM object, you essentially create another image instance. The original image (instance) remains unchanged; the new copy can be cropped, color-edited, annotated, and more. In short, it can be modified to become very different from the original. Yet it will still be labeled as “head X-ray of John Smith.” Which instance should be used by your radiologist? When you work in healthcare, you realize that it is in your best interest to be able to differentiate between the instances. In DICOM, this is achieved with Unique Identifiers (UIDs). Look at Fig. 5.10. It illustrates how one original image can produce multiple instances, some of them still being identical to the original, yet residing in different locations or serving different purposes. It also explains why UIDs, used to label those instances, must be globally unique. Image instances can be sent far away from the original (for example, for teleradiology reading in another country) where they will be stored with many other images in other archives. DICOM UIDs, just like human DNAs, work as unique markers that are always capable of identifying a particular instance wherever it might be (Fig. 5.10). For the same reason, unique identifiers are used in DICOM to tag not only individual images, but also image series, studies, devices, syntaxes in data exchange protocols, and many other things that you do not want to confuse.

Compressed instance for teleradiology

Manually-edited instance for review

Original image of Joe Smith on main PACS archive

Backup instance on another archive

Fig. 5.10 Instances of the same image; each needs a different UID

Cropped instance for report

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65

DICOM unique identifiers are strings such as “1.2.840.10008.1.2” built from numerical components separated by periods; that is, they conform to the UI type (see UI type in our VR table, Sect. 5.3). UID strings are supposed to be globally unique to guarantee distinction across multiple countries, sites, vendors, and equipment. In our globalized universe, this is the only way to ensure that your image or transaction does not run the risk of being confused with any other – no matter where it comes from. Therefore, DICOM uses the following UID encoding convention: UID = . Here, the < org root > portion of the UID uniquely identifies an organization, (that is, manufacturer, research organization, NEMA, and so on). Ideally, each organization is supposed to apply and receive its own root ID (which does not always happen) to guarantee that this root is not in use (see Annex C, DICOM PS3.5, for the root registration rules). Also, the 1.2.840.10008 string is reserved as the universal < org root > for all DICOM transaction UIDs, and cannot be used elsewhere.

Tip: Recognizing DICOM Files

Open an unknown file in WordPad, and search it for 1.2.840.10008. If this string is found, you are likely dealing with a DICOM file. The four DICM letters at the very top of the file (characters 129–132, counting from the file start) are another way of identifying DICOM files. However, some old or incorrect DICOM implementations might not write them, while multiple UID prefixes like 1.2.840.10008 should be easier to spot.

The < suffix > portion of the UID is also composed of a number of numeric components, and “shall be unique within the scope of the < org root>” (see section 9 in DICOM PS3.5). While roots are relatively short, these are the suffixes that are used to capture the uniqueness of the instance. For example, if you build a suffix like: . < study ID > . < current date > . < current time in milliseconds>

you could be pretty sure that no one in your enterprise will have the same UID. This is why all 64 UI-type characters come in so handy; the more characters you use, the fewer your chances of having two equal UIDs. For the same reason, UIDs are often used as DICOM file names. This is not really DICOM-compliant (see Sect. 10.1.4). True DICOM file names should come from 8-character components containing only capital letters, digits, and underscore characters (for example, DIR1\SKW_12AB5). However, if the 8-character format is mandatory for file-exporting applications (writing DICOM files to external, removable media such as CDs/DVDs or flash drives – see Chap. 10), UID-based names are widely used internally by nearly all DICOM applications to store their

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images on local hard drives. The UID-based DICOM file naming is justified: each DICOM image object includes the Image “SOP Instance UID” attribute (0008,0018). This makes it a good name for a DICOM image file, associating it with the unique object instance within. When you see file names such as: 1.2.804.114118.2.20040909.125423.3692976692.1.1.1 you are likely dealing with DICOM files. In essence, DICOM files are nothing more than DICOM objects stored on your hard drive – DICOM object memory dumps, if you wish.

UID Parsing?

Even though UID names are always built with certain logic in mind, do not try to parse them or use their names to convey additional information. For example, even if you know that a UID name might include patient ID or study date, do not try to extract this data. DICOM explicitly warns against this. UID names are not meant for any data exchange and their sole purpose is to distinguish between multiple object instances. As long as this is guaranteed, the DICOM application is free to change or update UID names as it finds appropriate with whatever additional naming logic it might be using. In addition to what DICOM prescribes, correct UID-issuing policy always remains on your DICOM manufacturer’s mind. They – through their DICOMcompliant software – must track image changes and create a new UID for each clinically different instance of the same image.

5.6

DICOM Information Hierarchy

Before we continue with our review of DICOM data, it is worth having a quick look at how DICOM structures its information. Certainly, DICOM Data Dictionary attributes play very important roles in mapping real-world data into the DICOM standard. But still, these attributes are many. Don’t we need to put them in some order? This order is achieved in DICOM with the Patient-Study-Series-Image hierarchy (Fig. 5.11): • One patient may have multiple studies. • Each study may include one or more image series. • Each series has one or more images. This hierarchy naturally reflects what happens in the real world when a patient needs to have some medical imaging done. John Smith comes to a hospital where several studies may be scheduled (for example, MR, CT, and US exams). Several follow-up studies could be needed later. Each study may have multiple image series (CT, ultrasound, 3D reconstructions, with varying imaging protocols, and so on). And each series, quite naturally, will have one or more images. Now, if we need to find or sort images for this patient,

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DICOM Information Hierarchy

67

Patient Patient ID (0010,0020)

Study Study Instance UID (0020,000D)

Study Study Instance UID (0020,000D)

Series Series Instance UID (0020,000E)

Image Image SOP Instance UID (0008,0018)

Series Series Instance UID (0020,000E)

Image Image SOP Instance UID (0008,0018)

Fig. 5.11 DICOM information hierarchy

we can do so based on their patient, study, series, and image attributes. For example, we might want to find all MR studies for this patient done over the course of the past year. To implement this hierarchy, DICOM assigns a key-level ID to each hierarchy level. For the patient level, this is Patient ID – all patients should have IDs that identify them uniquely. The “Patient ID” element is stored with the (0010,0020) tag, making this tag required for any type of imaging. Then the same principle applies to the other three levels of the Patient-Study-Series-Image hierarchy: at the Study level, each study has its unique “Study Instance UID” (0020,000D); at the Series level, each series has its unique “Series Instance UID” (0020,000E); and at the Image level, each image has its own “SOP Instance UID” (0008,0018). Note that Study, Series, and Image UIDs are formatted with the UI VR type defined as 64-character-long unique (instance) identifiers made out of digits and periods. Patient ID (also known as MRN, Medical Record Number), probably due to its frequent use in human interfaces, follows a less-structured LO (long string) VR type. It is also up to 64 characters long, but allows practically any character. All DICOM commands and most DICOM data attributes are always bonded with the 4-level information model. Thus, the four DICOM hierarchy attributes play one important role: just like their names suggest, they uniquely identify their data. For example, if two studies have the same Study Instance UID value, they are expected to be identical. Moreover, if two patients have the same Patient ID, they are expected to be the same person. When properly used, this substantially improves data identification. Otherwise, we can easily run into serious problems.

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Parlez-vous DICOM?

Problems with Patient ID

The use of Patient IDs was meant to eliminate obvious problems with using patient names for patient identification. To put it simple, patient names are not reliable because: 1. They can be misspelled (letters, commas, even blanks between the name parts). 2. They can be entered incorrectly (swapping first and last names, for example). 3. They can change (due to marriage, legal issues, and so on). 4. They can be hard to transliterate (typing foreign names on a DICOM unit that does not support a specific foreign alphabet. Consider entering Japanese names on an English-based CT scanner, for example). 5. They can violate patient privacy. With all this in mind, using an ID such as 12345XYZ to identify a specific patient is obviously a much better approach. Nothing, however, comes without problems in our real, imperfect world. First of all, there is no central Patient ID repository that would generate and maintain consistent Patient IDs. There are no universal ID rules either. When a patient shows up in a clinical center, it is always the center’s local policy that assigns patient IDs, if any. Some would use a patient’s social security number, date of birth, or anything else more or less patient-specific; in this case, at least the same patient ID can be used consistently at the next patient encounter. Others would simply use an alphanumeric code, or worse, a consecutive numeral in attempt to make it more secure. This “security” makes patient IDs location-specific (when it is impossible to use the same ID at another place) and event-specific (perhaps at the same place but at a later time). Most facilities would simply resort to using patient names as patient IDs, thus discarding the whole idea of Patient IDs. Moreover, some hospitals change Patient IDs depending on the modality where the patient is scanned, or the destination where the images will be sent for radiological reading. They can easily end up with “123.ForDrSmith” and “123.Followup” IDs, corresponding to the same 123 patient. Using those IDs quickly turns into a total nightmare; despite their intended purpose, they would never really identify anyone – at least not reliably. Furthermore, IDs are meant for unique data identification, and not for encoding information. We Are All Brothers and Sisters

As I already mentioned, all patients with the same Patient ID are expected to be the same person in DICOM. I recently ran into the most interesting case of patient ID misuse in one well-respected hospital. All patients arriving without ID entries were labeled with “W/I” patient ID, plainly standing for “without ID.” This was done daily for a large number of exams, essentially merging all these patients into one as far as DICOM was concerned. Consequently, PACS software was not able to distinguish between these patients at all. If no ID is available, even using patient initials with birth date (for example, JS19670102) would work much better than “W/I.”

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DICOM Information Hierarchy

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You can find another attribute in the DICOM Data Dictionary called “Other Patient IDs” (0010,1001). It was provided to mitigate the problem of multiple IDs for the same patient, but it is rarely used in DICOM software. The reason is obvious: it is much better to enforce a consistent single-ID policy than to deal with a list of different IDs for the same patient (possibly overlapping with other patients). Is there any way to deal with Patient ID inconsistencies? Practically speaking, yes. Good DICOM software never relies on the Patient ID attribute alone, and always implements some additional logic to verify patient identity. This logic is based on other data available in DICOM data elements. Patient name, date of birth, sex, last study date, weight, size, and a few other tags can be used to make intelligent conclusions about patient similarity and identity. For example, if all these parameters coincided for two patients with different patient IDs, there is a strong reason to believe that the patients are indeed the same person and that the IDs need to be reset to the same ID (the one used first). The same approach is used to merge patients with misspelled names and other data errors. Different patient merge functions are often implemented in PACS software. The same method is also used to split patients if they were erroneously merged (for example, if the same Patient ID was mistakenly assigned to two different people). I remember an imaging center where all Patient IDs were 3-digit numbers; but all you can do with the 000–999 numbers is to ID a thousand patients. When you run out of this thousand, you will have to start recycling the same IDs for the other patients, thereby erroneously merging different people. Using short, easy IDs can hardly make your work easier. Patient splitting and merging should not be done automatically. When a DICOM program detects that splitting or merging needs to be done, it should somehow flag the suspected records and notify its human administrator. No matter how complex the program logic might be, it could result in false merges and splits, and human assistance to avoid these problems is crucial. As the role of decentralized PACS increases and regional or national systems replace local ones, the demand for correct and consistent patient identification will surely grow. The use of smart cards, RF tags, and biological patient profiles (which can be done without violating patient’s privacy) to encode and differentiate between patients is already gaining support in medical IT.

Real Case: Twins

Once, I was involved in a case with twin sisters. Both came to the same doctor at the same time with the same right ankle problem. Both girls had the same last name (as you might expect), and to make things even more interesting, their name was about 20 letters long. When it was first entered in the standard “Patient Name” box on the tech’s workstation, there was no space left for their first names. Fortunately, the sisters were assigned different IDs, but the IDs were not descriptive – even the doctor could not tell whose images he was looking at. Do not consider this situation exceptional.

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Parlez-vous DICOM?

Problems with Study, Series, and Image UIDs

One good thing about Study, Series, and Images UIDs is that, unlike Patient IDs, they are almost never entered or assigned manually. Instead, they are autogenerated on modalities and automatically inserted into the DICOM tags of their images. Another good thing is that Study, Series, and Image data is collected during a single patient scan. With Patient IDs, we have to worry about maintaining them the same between the studies or at different locations. Study, Series, and Image UIDs are collected in a single, uninterrupted imaging procedure, which they identify. This also simplifies the method of generating Study, Series, and Image UIDs. For example, they can be created as current date/time strings written to the fraction of a second accuracy, as we saw in Sect. 5.5.8. This definitely makes the entire process more reliable, but not without a few very common problems as well. The most annoying problem arises when, for whatever technical reason, some of these IDs are generated incorrectly. For example, I have seen many times the situation with different Series Instance UIDs assigned to images from the same series. In any DICOM software, images are grouped in series solely based on their Series UID, so the series with inconsistent Series UID values would simply fall apart into sets of unrelated images. That is, instead of a CT series with 2000 images nicely ordered by time and 3D location, you might see 2000 separate images with no means to view them as a whole. If the image viewing software has image-sorting functionality based on image time, that feature can at least put the images in order temporarily, but this would be a mere band-aid on the problem. Unlike Patient ID, DICOM software rarely offers any merge function for series. So the only way of dealing with this problem is to fix it where it happens: on the modality unit. Another typical, but less problematic, dilemma arises from the uniqueness of the Series, Study, or Image (SOP) UID. The letter U in UID stands for Unique and qualifies the ID (Identifier) portion, requiring that identifier values be globally unique. If anything ever changes in an image (for example, you crop/rotate it, or you compress it with lossy compression), then the image is not the same anymore, even if all clinically important information remains the same. Consequently, Image (SOP) UID for this modified image must be changed (see Sect. 5.5.8). Do not be surprised if you see two image series that look exactly the same, but are still stored separately on your imaging archive; there could be a reason. It is good to have Study, Series, and Image IDs generated automatically on a modality, but what if this does not happen? The most typical example involves secondary capture DICOM images, such as scanned (digitized) films. When you digitize films, or import non-DICOM images into DICOM format, the relationship between several films or images are known only to you, and definitely not to your digitizer. Do the images belong to the same patient, study, series? Your DICOM digitizer should provide sufficient functionality for assigning Study, Series, and Image UIDs manually to record the original image relationships in the digitized DICOM data. Unfortunately, not all secondary capture devices treat this

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Modules, IODs and IEs

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requirement seriously. If you are planning on buying one, please verify that it has ample support for entering DICOM data, and for assigning the images to the same Patient, Study, or Series groups.

5.6.3

Hierarchical and Relational Data

To wrap up our discussion on the DICOM Patient-Study-Series-Image information model, let us briefly mention that it represents a central part of a more complex DICOM model of the real world. This model can be found in the standard (section 7 in PS3.3) adding more items to the basic Patient-Study-Series-Image hierarchy without changing its essence. Virtually all DICOM devices function on the Patient-Study-Series-Image steps, performing hierarchical data searches, retrievals, and transactions. To identify a series, you will need first to find the patient and the study the series belongs to. This logic is clearly reflected in all DICOM interfaces. They always start browsing their data from the top, from the highest Patient (sometimes Study) level, then gradually descend to Series and Images. Moreover, hierarchical DICOM queries and data retrievals will fail on any of the four Patient-Study-Series-Image levels if the information about the higher level IDs is not known. For example, a hierarchical search for a particular series will fail if its study and patient IDs are not known beforehand. The only DICOM alternative to hierarchical data processing is known as relational data processing (Fig. 5.12). Unlike hierarchical processing, relational data processing does not break data into the four levels, and it permits you to search data in any way possible (provided that the data is somehow related). For example, you can search for a particular series not knowing its Study and Patient IDs as long as you provide other data to identify the series (such as modality, date/time, UID, and so on). DICOM supports relational (nonhierarchical) data processing as an option. While hierarchical data processing is mandatory on any DICOM unit, relational data processing may or may not be provided in addition – and more often it is not. Whether relational data processing is available should be specified in the device’s DICOM Conformance Statement.

5.7

Modules, IODs and IEs

Building DICOM objects from data elements is the only way to do it right, but with some 2000 data elements in the DICOM Data Dictionary, we would like to have a bit more structure. You cannot really get a few MR-specific elements (such as (0018,0087) magnetic field strength), add a few CT tags (such as kvp in (0018,0060)), insert ultrasound images, and call this a DICOM object. The chimera will be rejected by most DICOM devices and even the remaining ones – marvels of DICOM data processing flexibility that they are – won’t have any idea how to process such a jumble. Data elements are the smallest building blocks, and they might not necessarily fit together. To put this in a better order, we need to group data elements into larger

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Hierarchical 1. Find all patients with name “John Smith”

Parlez-vous DICOM?

Relational Patient Level key: Patient ID, (0010,0020) Find all images with

2. For each found patient, find all studies from the last month

Study Level key: Study Instance UID, (0020,000D) Series Level

3. For each found study, select those with CT series

key: Series Instance UID, (0020,000E)

Patient Name=John Smith and Study Date=last month and Modality=CT

Image Level 4. For each found series, get the images

key: SOP Instance UID, (0008,0018)

Fig. 5.12 Example of hierarchical (left) versus relational search for “images of last-month CT study for John Smith.” Hierarchical searches are simpler. This makes them more efficient, but it requires more steps

building blocks, and use them to build more meaningful and more defined DICOM objects. DICOM calls these blocks Information Modules, Information Entities (IEs), and Information Object Definitions (IODs). Like many things in DICOM, they are related hierarchically. Modules form IEs, which in turn are used to build IODs. Because DICOM data is defined for each image modality the proper choice of modules, IEs, and IODs depends on the modality. Let us have a closer look at how it all works.

5.7.1

Attribute Macros: Making It Easier

Often, you might prefer to group similar attributes to shorten their referencing in other modules. DICOM calls these groups of common characteristics macro attributes (Table 5.14 ).

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Table 5.14 Series and instance reference macro attributes Attribute name Tag Attribute description Referenced Series (0008,1115) Sequence of Items each of which includes the Sequence Attributes of one Series. One or more Items shall be present >Series Instance UID (0020,000E) Unique identifier of the Series containing the referenced Instances >Referenced Instance (0008,114A) Sequence of Items each providing a reference to Sequence an Instance that is part of the Series defined by Series Instance UID (0020,000E) in the enclosing Item. One or more Items shall be present >> Referenced SOP (0008,1150) Uniquely identifies the referenced SOP Class Class UID >> Referenced SOP (0008,1155) Uniquely identifies the referenced SOP Instance Instance UID

Table 5.15 Patient identification module Attribute name Tag Patient Name (0010,0010) Patient ID (0010,0020) Other Patient IDs

(0010,1000)

Other Patient Names Patient’s Birth Name Patient’s Mother’s Birth Name Medical Record Locator

(0010,1001) (0010,1005) (0010,1060) (0010,1090)

Value/description Patient’s full name Primary hospital identification number or code for the patient Other identification numbers or codes used to identify the patient Other names used to identify the patient Patient’s birth name Birth name of patient’s mother An identifier used to find the patient’s existing medical record (for example, film jacket)

Note the possible use of sequencing (see Sect. 5.3.10). The “greater than” sign ( > ), as always, indicates that the next element is embedded into the previous one with the SQ VR. But even without sequencing some macros can be pretty large and can take several pages in PS3.3 (Image Pixel macro, for example). As far as information structuring is concerned, macros do not correspond to any particular object or data block; they help us avoid repetitive element tables.

5.7.2

Information Modules: Basic Data Blocks

Modules, as I just said, provide the very first and the most essential level of data element organization. For example, Patient Identification Module (Table 5.15 ) groups all patient-identifying information: data tags for patient name, ID, birth name, maiden name, and more. The contents of this module are self-explanatory: they capture all possible attributes (data elements) related to patient identification. Clearly, not all of them can be

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Table 5.16 Clinical trial module Attribute name Tag Clinical Trial Sponsor Name (0012,0010) Clinical Trial Protocol ID (0012,0020) Clinical Trial Protocol Name (0012,0021) Clinical Trial Site ID (0012,0030) Clinical Trial Site Name

(0012,0031)

Clinical Trial Subject ID

(0012,0040)

Clinical Trial Subject Reading ID

(0012,0042)

Parlez-vous DICOM?

Attribute description The name of the clinical trial sponsor Identifier for the noted protocol The name of the clinical trial protocol The identifier of the site responsible for submitting clinical trial data The name of the site responsible for submitting clinical trial data The assigned identifier for the clinical trial subject. Shall be present if Clinical Trial Subject Reading ID (0012,0042) is absent. May be present otherwise Identifies the subject for blinded evaluations. Shall be present if Clinical Trial Subject ID (0012,0040) is absent. May be present otherwise

collected for a given patient, and in some cases none can be known at all (unconscious patients, for example). However, modules are not meant to contain complete values for all their attributes. Unknown fields can be left blank or even omitted if not required. The main purpose of modules is to gather related data attributes (elements) in a consistent, structured manner. For example, if our patient becomes a subject of a clinical trial, we add a Clinical Trial module to record all trial attributes (Table 5.16). To give you a slightly different module example, let’s look at the Cine module in Table 5.17, which defines playback parameters for multiframe images. If a DICOM object contains a sequence of video frames (a sequence of images that are consecutive frames of a digitized video), a Cine module stores the information on how this sequence needs to be played. The most essential parameter here would be “Frame Time” (0018,1063), representing the time delay in milliseconds between the frames. For example, a 25 frames per second average video rate corresponds to 1000/25 = 40 milliseconds in (0018,1063). However, as you can see, a number of other attributes can be supported depending on the data content. Note that the Cine module uses the Code Sequence macro. Just like almost anything in DICOM, information modules can be mandatory (indicated in DICOM by a capital M); conditional – required if other specific modules are present (indicated by a capital C); and user-defined – think about proprietary data elements (indicated by a capital U). The proper selection of modules depends on the type of data stored in the DICOM object. In most cases, this type corresponds to the image modality (CT, MR, CR, and so on); but there are also non-modality object types (think about ECG, reports or voice recordings). For example, Patient Identification Module is always mandatory for any DICOM modality; we cannot have a digital image without knowing who it belongs to. The Cine module, as you would imagine, is required

5.7

Modules, IODs and IEs

Table 5.17 Cine module Attribute name Tag Preferred Playback (0018,1244) Sequencing

Frame Time

(0018,1063)

Frame Time Vector

(0018,1065)

Start Trim

(0008,2142)

Stop Trim

(0008,2143)

Recommended Display Frame Rate

(0008,2144)

Cine Rate Frame Delay

(0018,0040) (0018,1066)

Image Trigger Delay

(0018,1067)

Effective Duration

(0018,0072)

Actual Frame Duration Multiplexed Audio Channels

(0018,1242) (003A,0300)

Description Code Sequence >Channel Identification (003A,0301) Code

>Channel Mode

(003A,0302)

>Channel Source Sequence >> Include ‘Code Sequence Macro’

(003A,0208)

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Value/description Describes the preferred playback sequencing for a multiframe image. Enumerated Values 0 = Looping (1,2…n,1,2,…n,1,2,....n,…) 1 = Sweeping (1,2,…n,n-1,…2,1,2,…n,…) Nominal time (in ms) per individual frame. Required if Frame Increment Pointer (0028,0009) points to Frame Time An array that contains the real-time increments (in ms) between frames for a multiframe image. Required if Frame Increment Pointer (0028,0009) points to Frame Time Vector The frame number of the first frame of the multiframe image to be displayed. The Frame Number of the last frame of a multiframe image to be displayed Recommended rate at which the frames of a multiframe image should be displayed in frames per second Number of frames per second. Time (in ms) from Content Time (0008,0033) to the start of the first frame in a multiframe image Delay time in milliseconds from trigger (for example, X-ray on pulse) to the first frame of a multiframe image Total time in seconds that data was actually taken for the entire multiframe image Elapsed time of data acquisition in ms per each frame Description of any multiplexed audio channels. Required if the Transfer Syntax used to encode the multiframe image contains multiplexed (interleaved) audio channels, such as is possible with MPEG2. Zero or more items may be present in this sequence

A reference to the audio channel as identified within Transfer Syntax encoded bit stream (1 for the main channel, 2 for the second channel, and 3 to 9 for the complementary channels) A coded descriptor qualifying the mode of the channel. Enumerated Values MONO = 1 signal STEREO = 2 simultaneously acquired (left and right) signals A coded descriptor of the audio channel source. Only a single Item shall be permitted in this sequence Defined Context ID Audio Channel Source 3000

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for multiframe ultrasound images (such as ultrasound loops), but not for CT or MR (none of these produces digital videos). Also note (especially if you are familiar with database design) that DICOM modules are not normalized; that is, different modules may share the same attributes (have overlapping data). For example, important attributes such as “Patient Name” (0010,0010) can be found in several DICOM modules, and the Patient module (see DICOM PS3.3) contains most of the data from the Patient Identification module shown in Table 5.15. In fact, even attributes inside the modules can be redundant. For example, in the Cine module in Table 5.17, “Frame Time” (0018,1063) and “Cine Rate” (0018,0040) are clearly dependent (Cine Rate = 1000/(Frame Time)). This is not the most perfect way of structuring complex data because it makes some modules more dependent on others. However, it is understandable in both the practical and historical sense of an evolving standard when dealing with complex real-world relationships. Part 3 of the DICOM standard contains specifications for all standard modules (there are about 100 of them) and a large table of Information Object Modules, detailing which modules are appropriate for each modality.

To DICOM Developers

Are you designing your own DICOM software? Then I would really recommend that you use DICOM modules as your fundamental data building blocks. Start by creating a parent Module class that employs general data element encoding (writing and reading DICOM objects with explicit and implicit VRs) and develop a child class for every specific DICOM module that you might need. Do not implement all 100 of them! With clear class design, you can add new ones as needed with minimal coding effort. For example, start with the most essential modality type (such as CT) and code all classes involved in the CT imaging modules (from the Information Object Modules table in PS3.3), then expand to new modalities (modules) when necessary. For overlapping modules, add cast operators to convert them into each other. For redundant data attributes (such as Frame Time and Cine Rate in Table 5.17 ), support only the minimal attribute subset and recover the rest with proper accessor functions. In brief, use the full power of object-oriented programming and your code will be clean and efficient. In all examples of DICOM software that I have seen, DICOM modules have been almost certainly neglected. Objects were built straight from the data elements (attributes) leading to inefficient and error-prone implementations. Let’s hope that DICOM workgroups keep polishing the modular design of the standard, making it more normalized and complete.

5.7

Modules, IODs and IEs

5.7.3

77

Information Entities

DICOM Information Entities (IEs) are built from DICOM Information Modules. Building IEs from Information Modules is very straightforward. For each IE, DICOM simply lists a few modules it should include. For example, a Common Patient IE should include Patient Module, Specimen Identification Module, and Clinical Trial Subject Module. A Common Study IE includes General Study Module, Patient Study Module, and Clinical Trial Study Module. As we descend to series and images where the differences between various modalities become more essential, IEs become a bit more complex, involving more modules. Why would we need IEs if we already have Information Modules? IEs represent the next level of complexity in the DICOM information model. If modules were meant to combine related data elements (attributes), IEs were designed to represent real-life entities involved in the medical imaging workflow. The best way to understand IEs is to look at this workflow as viewed by DICOM (PS3.3). All boxes in Fig. 5.13 correspond to DICOM IEs. A patient comes to have a study on some equipment; and the latter produces a series of images, but may also include other data (for example, waveforms for sounds, structured reports to capture report data, frame of reference to record image position and orientation in space and time, and so on). There are some 20 DICOM IEs; their list is being constantly updated by DICOM workgroups and you can discover them all in the most current version of DICOM (PS3.3).

5.7.4

DICOM Information Objects

When combined meaningfully, IEs build Information Object Definitions (IODs). IODs crown the DICOM information hierarchy; they define the objects used by DICOM. In other words, the entire DICOM data processing is done in terms of IOD and all DICOM data must conform to prescribed IOD types. Consequently, IODs are formed to represent the most common data types in digital imaging – such as images from various modalities. Table 5.18 shows how we could build a CT image IOD from a set of appropriate IEs and Information Modules. By replacing the “CT image” information module in this IOD with “MR image,” we get the MR IOD. But to get, for example, an NM (Nuclear Medicine) IOD, we would need to make many more changes to represent NM isotopes, detectors, multiframing, and so on.

5.7.5

IODS and Their Instances

The DICOM standard was built with object-oriented design in mind. It attempts to create its own model of the real world, representing data with abstract objects, defined as sets of clinically important attributes. Consider any real-world entity in

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Patient

has a

Study Equipment

creates

contains

Frame of Reference

Series

spatially or temporally

Real world value mapping

Encapsulated document Fiducials

Registration

Raw data

contains

Spectroscopy

Structured report

Presentation state

Waveform

…… (more IEs) Image

Fig. 5.13 DICOM information model

your medical environment: a patient, for example, our favorite fellow named John Smith. From the medical point of view, you probably do not care about John’s haircut, or the name of his dog. But you would certainly like to know his name, ID, date of birth, sex, weight, insurance provider, and a wealth of other clinically important data. In fact, as far as the medical workflow is concerned, John Smith is nothing but a collection of all his attributes used in medical imaging, as shown in Fig. 5.14. (Of course we care about John as a person, but this is not a study in social dynamics.) This reasoning returns us directly to the idea of DICOM IODs. For any class of real-world entities (patient, CT image, DICOM film printer), its IOD is nothing but a set of attributes (grouped into modules and IEs) adequately describing the class for DICOM purposes. When we have a particular patient (John Smith), CT image, and so on, they become instances of abstract IODs as they assign particular values to IOD attributes.

5.7

Modules, IODs and IEs

Table 5.18 CT IOD IE Patient

79

Information module Patient Clinical Trial Subject General Study Patient Study Clinical Trial Study General Series Clinical Trial Series Frame of Reference General Equipment General Image Image Plane Image Pixel Contrast/bolus

Study

Series Frame of Reference Equipment Image

Usage M (mandatory) U (user-defined) M U U M U M M M M M C (conditional) – Required if contrast media was used in this image M U U M

CT Image Overlay Plane VOI LUT SOP Common

Patient in

Modules

IEs

IODs

(data elements)

DICOM Data Dictionary

Real world

Instance of Patient IOD

Name

John Smith

ID

123456

DOB

19681108

Weight

75.5

Sex

M

IOD (attribute list)

Patient Information Object Definition

IOD instance (attribute values)

Fig. 5.14 From patient to patient object

Furthermore, DICOM breaks all IODs into Normalized and Composite. A normalized IOD represents a single, real-world entity just as our Patient IOD represents a patient; all attributes of a normalized IOD are inherent in the real-world entity it represents. The DICOM Study IOD, for example, is normalized and contains only inherent study properties such as study date and time. Patient name, for example, being a property of a patient rather than a study, would not be present in the study IOD.

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Composite IODs are mixtures of several real-world entities or their constituent parts. Consider a CT image IOD, for example: in DICOM, this IOD will contain some of the patient attributes (name, ID, and so on to identify the patient the image belongs to) along with attributes of the CT scanner, patient study, and more. This blend from several real-world entities makes a CT image an IOD composite. As you might guess, composite IODs work better for capturing relationships, associations, processes, and contexts. Normalized IODs are meant to represent single objects. However, the line between composite and normalized is often blurred, and as far as we are concerned they all remain IODs, equally treated by DICOM. To summarize, IODs are abstract representations of real world objects – patients, CT images, and more. In terms of object-oriented design, IODs are objects, or classes. Any actual DICOM objects that we discussed earlier (Sect. 5.5) are nothing more than IOD instances recorded in DICOM format where VRs are used to record data element names and values.

5.7.6

Learning More

I presented you with the basic concepts of DICOM data organization. Now you know enough to be able to understand DICOM from VRs to IODs. If you want to go any further, part three of the DICOM standard (PS3.3) is your ultimate reference. It applies the principles you have learned to define all particular objects and data types used in DICOM. It will also give you more details on DICOM information models and the way DICOM structures radiology workflow by binding it with IODs and IEs. Bear in mind that part three is the largest part of the standard; it is well beyond 1000 pages already and grows by some 100 pages a year. Most of the changes in this document are recorded in its annexes: A (composite IODs), B (normalized IODs), and C (information modules). Just reading them continuously could probably get you into the Guinness Book of World Records; they go on and on with specific IOD types. Instead, try to data mine PS3.3, searching by your keywords. Unfortunately, the evolutionary approach to DICOM writing sometimes takes ill turns; one cannot indefinitely improve the standard by growing the number of its object definitions. If DICOM 4.0 ever happens, maybe the entire assembly of IEs, modules, and IODs will be restructured into a more normalized and condensed form.

6

Medical Images in DICOM

Let me assume, my dear reader, that the time we used to wander in the land of VRs and DICOM objects has been well spent, and that you have a much better idea of how DICOM deals with medical data. This prepared you for the next important step in your voyage: looking at how DICOM works with medical images. Certainly, images possess some well-known properties (width, height, bits per pixel), which can be found in the DICOM Data Dictionary, and which DICOM encodes with explicit or implicit VRs, as you have already learned. But the most interesting image attribute is the image itself – the sequence of image pixel values – that DICOM stores in the standard (7FE0, 0010) Pixel Data tag, using either OB (for 1-byte pixel samples) or OW (for 2-byte pixel samples) encoding. DICOM supports a wide range of image formats for storing these (7FE0, 0010) pixels. The formats can be loosely broken into two main groups: 1. DICOM-specific: the formats that are used by DICOM only. They are typically the oldest ones, introduced at the dawn of the computer era before better imagestoring approaches had been developed. They resemble raw bitmaps with varying ways of packing the pixel bytes. 2. Independent standard formats accepted by DICOM. These include such wellknown formats as JPEG, RLE (run-length encoding), ZIP, and the less known (but getting more and more popular) JPEG2000, and JPEG-LS. All these standards are also associated with various image compression techniques, reversible and not, which makes them particularly useful in medical imaging (reducing image data size is important). When DICOM includes other standards to be used for particular tasks (such as JPEG for image encoding), the embedded standard approach is very convenient, consistent, and makes good practical sense. Let us start with the first type, because it is the easiest and the oldest, and because it is still used most of the time as the DICOM default. Then we’ll review the most important points of the independent standards. You can read more about them elsewhere, but some of their properties have tremendous effects on medical imaging storage and analysis, so I will try not to miss them.

O.S. Pianykh, Digital Imaging and Communications in Medicine (DICOM), DOI 10.1007/978-3-642-10850-1_6, © Springer-Verlag Berlin Heidelberg 2012

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Fig. 6.1 Zooming into digital image

Medical Images in DICOM

Zoomed image fragment Pixel

6.1

DICOM Bitmaps

A digital image, as you might already know, is a rectangular matrix of pixels (picture elements) – tiny dots of different colors that form the actual picture. For example, a typical CT image is 512 pixels wide and 512 pixels high; that is, it contains 512 × 512 = 262144 pixels. If you write these pixels line-by-line starting from the top left corner you will have a sequence of 262144 pixel values, which you can store in a file. Essentially, this file is your raw bitmap image (Fig. 6.1). Now close your eyes and make a mental list of all digital image attributes that you consider important. Are you done? Compare yours to DICOM’s: • Image width and height – Definitely, you need to know them. Their product is often referred to as spatial image resolution and equals the total number of pixels in the image. • Samples per pixel – Each image pixel can be a blend of several sample values. The most typical case is a color pixel, which comprises three independent color samples: red, green, and blue (known as the RGB color space). While the value of each sample contributes to the pixel’s brightness, their mixture creates the color. For example, mixing equal quantities of red and green produces shades of yellow and equal quantities of red, green, and blue correspond to grayscales. Grayscale images, however, are usually stored with one monochrome sample per pixel, corresponding to the pixel’s grayscale luminance. When 2-byte (16-bit) samples are used (OW VR), this provides for 216 = 65536 possible grayscales – the source of DICOM support for deep grayscales in CT, MR, CR (X-ray), and many other modalities. In any event, the choice of pixel sampling remains constant for all pixels in an image, depending only on the imaging technique.

6.1

DICOM Bitmaps

83

• Bits used to store a pixel sample, Bs – DICOM calls this parameter “bits stored.” For example, if you have a grayscale image where each pixel (sample) uses eight bits, then Bs = 8, and the number of grayscale shades in this image will be 2Bs = 28 = 256. If you increase the number of stored bits to Bs = 10, you will have 210 = 1024 shades per sample, and so on. As you can see, the bits stored number is responsible for the color depth1 of the image; it shows how rich your image is. In this respect, this attribute is similar to spatial resolution (image width and height), but in the luminance domain – the more, the merrier. It is particularly valuable and important in digital medicine where you often need to see minute changes in colors and brightness. • Bits allocated per pixel sample, Ba – essentially, Bs, rounded up to a multiple of eight (to fit into whole bytes). This is how much computer memory will be used to store a pixel sample. Clearly, Bs £ Ba. • High bit, Bh – as we will see in a minute, this bit corresponds to the end of the Bs pixel sample within the Ba segment. Figure 6.2 summarizes the structure of a multi-sample DICOM pixel. The example pixel comprises three samples: red, green, and blue. In this particular example, each sample has Bs = 12 bits. Because all computer data is stored in bytes (1 byte = 8 bits), the 12 bits are rounded up to Ba = 16, and Bh = 11. Usually Bh = Bs − 1. The same 16-bit storage is allocated for the other samples (red and blue in this case), and the entire image is written as a sequence of its pixel samples. I hope this does not seem difficult, but to make this picture complete, let me show you another example of pixel sample encoding in Fig. 6.3. In this case, a sample was stored in Bs = 10 bits, but counting from bit two. Because DICOM stores the values of high bit Bh and bits stored Bs, we can recover the first “low bit” position as Bh + 1 − Bs and the rest will follow. The trickiest part in all “old school” pixel encoding is how DICOM can take advantage of the unused bits. If we look at the second example, we see Ba - Bs = 6 bits (bits 0–1 and 12–15) not containing any pixel data; so we wasted 6/16 = 37.5% of the storage space. To recoup this space, DICOM used to store additional information there, such as image overlay pixels (which are not particularly related to the original image pixel data). In essence, pieces of one information stream were jammed between the pieces of others just to use all the bits in every byte. This approach was popular in the early DICOM days when image compression was still in its cradle, and storing other data between the pixel samples was considered a cool way of optimizing the storage. As you can imagine, in reality it lead to rather confusing bit mixtures that eventually made themselves obsolete. These days, one can achieve much better memory usage with image compression than with chasing stray inter-pixel bits. Nevertheless, “bit squeezing” techniques are still standard

1

By color depth, I mean both the number of colors available in the color image and the number of grayscale shades available in the grayscale image.

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Medical Images in DICOM

(7FE0,0010) Pixel Data R1

G1

B1

R2

Pixel1

G2

B2

…

Pixel2

Ri

Gi

Bi

RN

…

Pixeli

GN

BN

PixelN

RGB Pixel

Red sample

Blue sample Green sample

…..

…..

Low

High

bit

bit Bh

0

1

2

3

4

5

6

7

8

9

10

Byte 1

11

12

13

14

15

Byte 2 Bits allocated Ba = 16

Bits stored Bs = 12

Fig. 6.2 Storing pixel sample bits: basic

in DICOM, and can be found in some applications and data, especially if you deal with an older DICOM unit – be aware of them. Annex D in DICOM PS3.5 of the standard explains other possible sample encoding schemes. If you can deal with the various ways of recording image pixels (samples), you know how to read and write DICOM bitmaps. As I have mentioned, DICOM treats image information like anything else and all important image attributes are translated into DICOM VRs stored in DICOM objects. These attributes must be present in any image-containing DICOM object. In DICOM parlance, these attributes are required. Table 6.1 provides a more detailed snapshot of some important image attributes, taken from the DICOM Data Dictionary. This is but a fraction of all DICOM pixel-related tags, but if you have any experience working with other image formats, you should appreciate the completeness of DICOM. For example, as (0028,0008) suggests, you can store a sequence of frames

6.1

DICOM Bitmaps

85

Sample

0

1

Low

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bit

Bh

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Byte 1

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Byte 2 Bits allocated Ba = 16 Bits stored Bs = 10

Fig. 6.3 Storing pixel sample bits: more complex

Table 6.1 Important image attributes in DICOM Data Dictionary Tag Name VR (0018,0050) Slice Thickness DS (0018,0088) Spacing between Slices DS (0020, 0032) Image Position DS (0020,0037) Image Orientation DS (0028,0002) Samples per Pixel US (0028,0004) Photometric Interpretation CS (0028,0008) Number of Frames IS (0028,0010) Rows US (0028,0011) Columns US (0028,0030) Pixel Spacing DS (0028,0100) Bits Allocated Ba US US (0028,0101) Bits Stored Bs US (0028,0102) High Bit Bh (0028,0103) Pixel Representation US (7FE0,0010) Pixel Data OW/OB

VM 1 1 3 6 1 1 1 1 1 2 1 1 1 1 1

(essentially, a digital video) in a simple DICOM image. You can specify the physical dimensions of pixels in (0028,0030) – for example, 0.7 × 0.7 mm in CT image – which allows you to measure the image objects in their physical units, such as millimeters. Knowing pixel spacing, spacing between the images (0018,0088), image position (0020, 0032), and orientation (0020,0037)allows for the most elaborate 3D reconstructions because we can preserve the correct sizes and locations of the objects.

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I could go on with this list almost indefinitely, but let me stop here and conclude the overview on DICOM bitmaps; after all, we do have bigger fish to fry. Just keep in mind that DICOM provides extremely rich support for storing image data and all related parameters, which no other image format can provide. This explains the success of DICOM in medical imaging. This also explains why exporting DICOM images in other “conventional” formats (such as 8-bit JPEG) always leads to losing critical image data and can result in potentially dangerous image misinterpretation. Besides, exporting in this manner simply does not make any sense. DICOM supports JPEG and other compression methods internally to compress pixel buffers in the (7FE0,0010) “Pixel Data” attribute within DICOM objects without sacrificing any other attribute data. Let us take a look at all this image compression stuff; maybe I can show you what all the hubbub’s about.

6.2

Image Compression

Consider a typical CT image with width = height = 512, grayscale (one sample per pixel), and bits stored = 12. How much computer memory would it take to store this image? Well, let us do the math. For storing 12 bits, we need to allocate two full bytes (16 bits). The total number of pixels is 512 × 512, so the total number of bytes to store them will be: 512 × 512 × 2 = 524,288 That is, we would need half a megabyte for the pixel data stored in the (7FE0,0010) “Pixel Data” attribute of the DICOM CT object. However, the CT images do not come alone; they come in series and studies, each containing anywhere from a few hundred to a few thousand images. In our case, a mere 200 CTs will occupy 100 megabytes. This will take nearly 6 h to download on a dial-up network, or several boring minutes on a broadband. A few days of studies like this can easily fill your hard drive. How do you deal with such data volume (Table 6.2)? Typical replies might be “Buy a better computer,” or “Get a faster network.” While upgrading system hardware is always a good idea, in this case it actually solves only one problem: how to spend your money. Look at the tremendous progress in the quality and resolution of digital image acquisition devices. DRs and CRs, multi-slice CTs, enhanced (higher-resolution) imaging, digital mammography, and ultrasound devices keep increasing the resolution and the number of images produced, which inevitably translates into larger image and study sizes (Branstetter 2009). If you plan to run a competitive and scalable medical enterprise, and you look into applications such as teleradiology, you won’t be able to survive without image compression (Huang 2004). Given this, image compression has been included in the DICOM standard since its inception. Image compression targets the (7FE0,0010) image pixel bytes, and

6.2

Image Compression

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Table 6.2 Typical sizes of digital images and studies Image modality Typical image matrix (height Image size, width, bytes per kilobytes pixel) (KB) Nuclear medicine, NM 128 × 128 × 1 16 Magnetic resonance, MR 256 × 256 × 2 128 Computed tomography, CT 512 × 512 × 2 512 Color ultrasound, US 600 × 800 × 3 1400 Computed radiography, CR 2140 × 1760 × 2 7356 Color 3D reconstructionsb 1024 × 1024 × 3 3000 Digital mammography, MG Up to 60000 6400 × 4800 × 2

Typical number of images in a studya 100 200 500 500 4 20 4

Typical study size, megabytes (MB) 1.5 25 250 680 30 60 240

a

Because this varies greatly depending on facilities, studies, and protocols, I provide the most general “order of magnitude” numbers b “Secondary capture” (SC) reconstructions; offered more often nowadays by CT and MR modalities

cleverly rearranges them into a much shorter form. This significantly reduces the original image size (much better than old style bit squeezing), which in turn saves storage space and often reduces image download time. I have already mentioned that DICOM does not invent image compression techniques of its own. Rather, DICOM includes nearly all well-known image compression algorithms including RLE, JPEG, JPEG2000, JPEG-LS, and ZIP. All these algorithms have been developed separately, resulting in separate ISO2 standards and applications; DICOM simply adopted them. All DICOM data objects with compressed (7FE0,0010) pixel buffers are encoded with an explicit VR Little Endian type (see Sect. 5.5.1). The use of image compression could have a significant effect on the image appearance and on the overall performance of PACS, so you need to know compression basics regardless of your current role in your medical enterprise. The main idea of data compression is not difficult at all. Any compression algorithm will try to find and trim the most redundant and repetitive information, thus making the data shorter. The efficiency of this trimming is quantified by the following compression ratio: R comp =

Original data size Compressed data size

The higher the ratio, the better compression is achieved. Each compression algorithm offers it own strategy to maximize Rcomp, but conceptually and practically, all compression techniques can be assigned to one of two categories: lossless (reversible) and lossy (irreversible) compression.

2

International Organization for Standardization.

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6.2.1

Medical Images in DICOM

Lossless Compression

Lossless compression does not change the image. After you compress and uncompress an image, you always get the original – pixel by pixel. This is done with clever regrouping and renaming of the pixel bytes. Just to grasp the general idea, consider the following sequence of pixel values: 1000, 1001, 1002, 1002, 1000, 1000, 1001, 1057,…. A typical lossless compression algorithm will try to explore the repetitiveness of the most frequent values like 1000 and replace them with shorter symbols. For example, if we replace “1000” by “a”, the original string turns into: a, 1001, 1002, 1002, a, a, 1001, 1057,…. Because “a” is shorter than “1000,” our entire data string has become shorter, and as long as we remember that we used “a” to compress the value of “1000,” we can always uncompress this string to its original value. This compression approach is known as “variable length coding.” Now think about this: if we take the most repetitive elements in the original data, and replace them with the shortest possible symbols, we could achieve substantial data size reduction, but we will always be able to recover our data. Well then, we just rediscovered the Huffman compression algorithm – the most popular algorithm in variable length coding (Oakley 2003). All natural images always have something repetitive to compress. First, neighboring pixels often tend to have similar values; second, an image can have large areas with the same pixel value (such as a black background). This intrinsic redundancy leads to successful lossless compression. When this redundancy is explored in an increasing number of available image dimensions – in x and y for a regular 2D image, or in x, y, and z for a 3D image set (such as CT slice sequences) – then more substantial compression can be achieved. Nevertheless, pixel repetitiveness can be exploited only to a certain extent, and for an average medical image Rcomp will be somewhere between two and four; meaning that the compressed image size will be between one-half and one-quarter of the original. This is already good, but not impressive if you have a lossy compression option.

6.2.2

Lossy Compression

Lossy compression, as its name suggests, sacrifices some of the original information to achieve a much higher compression ratio Rcomp. This sacrifice is made to force additional data redundancy so it can be efficiently complemented with the lossless compression step. Let us take the same pixel sequence as before: 1000, 1001, 1002, 1002, 1000, 1000, 1001, 1057,….

6.2

Image Compression

89

If the pixel intensity is about 1000, would you be able to visually perceive the difference between 1000 and 1001? Probably not because this is only a 0.1% change in the pixel brightness. Therefore, we can slightly modify this sequence by choosing one grayscale level as an acceptable error margin and replacing 1001 with 1000: 1000, 1000, 1002, 1002, 1000, 1000, 1000, 1057,…. If we apply the lossless compression to this modified sequence, as we discussed earlier, we would get: a, a, 1002, 1002, a, a, a, 1057,…. The result is much shorter than with pure lossless compression. In other words, if lossless compression is taking advantage of the equal pixels, lossy is extending this to the nearly equal pixel values within a hard-to-notice error margin. Believe it or not, current state-of-the-art lossy compression algorithms can have Rcomp as high as 100 – that is, reducing data to 1% of its original size! For average medical images, reasonable Rcomp values would be around 10 and sometimes up to 20. The value of Rcomp in the lossy compression entirely depends on the value of the perceptually-acceptable error. In the above example, we compromised one grayscale, but using 2-grayscale error would produce an even better compression result of: a, a, a, a, a, a, a, 1057,…. Obviously, this error margin cannot be increased indefinitely. At some point it does become perceivable, introducing visible artifacts in the lossy-compressed image. Balancing lossy compression between high Rcomp and visible image degradation has become an art in itself, but remember the following: 1. Lossy compression could lead to legal disputes. If lossy compression is used in the program, DICOM (and FDA) require that all lossy-compressed images be labeled as such. 2. Computer-Aided Diagnosis (CAD) issues. As CAD gains more popularity, computers and computer software become more involved in medical image analysis. Errors invisible to human eyes will be visible to CAD software and can become destructive.

CAD and Lossy Compression

Your CAD program, if you have one, can become an objective tool for evaluating the appropriateness of lossy compression. If it produces the same outcomes on the original image and the image after lossy compression, then you may use it as a good indication that the images were not over-compressed, and the lossy compression ratio you use at least does not interfere with your CAD analysis.

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Fig. 6.4 Illustrating excessive lossy compression with images and text patterns. Overdone JPEG creates highly visible blocking artifacts; overdone JPEG2000 creates blur

At the present time, lossy image compression has become popular in various teleradiology systems where images must be transmitted over long, sometimes bottlenecked networks with unpredictable bandwidth. But even there, it should be used with moderation. Last year, I saw one teleradiology service using lossy JPEG with 70% quality for medical image exchange. In case you do not know, you wouldn’t use 70% JPEG even for your pets’ pictures. The lossy compression artifacts at this quality level can become very apparent. If you plan to use lossy compression in your enterprise, always evaluate it with a trained radiologist for each image type/modality and make sure that the loss is indeed invisible. Too much lossy compression will produce highly visible image artifacts – as you can see in Fig. 6.4. The image on the left has been overcompressed with JPEG, which lead to the notorious JPEG checker artifacts – easily perceptible, rectangular blocks with constant color. The same image on the right has been exposed to large doses of JPEG2000 lossy compression; instead of checkers, this led to the image blur. The Rcomp for both images was about 70. Compressing them to a tenth of the original size would have produced a much better result, visually identical to the original image. Bear in mind that responsibility for using lossy compression rests on the radiologist’s shoulders; and the radiologist will be to blame if something is misinterpreted due to image quality loss. PACS manufacturers are only responsible for: 1. Consistently providing noncompressed images and providing compression as an option. Using the lossy option is your choice. 2. Clearly labeling lossy-compressed images as such when they are displayed on the viewing workstation. In this case, if you are not satisfied with the image quality, you should be able to reload the images as uncompressed (always confirm this with your PACS vendor).

6.2

Image Compression

91

Furthermore, repetitive application of lossy compression can severely degrade image quality. If you passed the original CT image through a single lossy compress-uncompress cycle using a reasonable compression ratio (for example, Rcomp < 10) the decompressed image will look exactly the same as the original (a trained radiologist will not see the difference). However, if you repeatedly compress and decompress this image (for storage or for networking), the quality loss and compression artifacts become cumulative. This can easily happen if you have a long chain of interconnected imaging devices, each with lossy compression enabled. By the time the image passes through all of them, it could become totally useless for diagnostic work. So, if you enable lossy compression, do it only at the final point (for example, long-term imaging archive or end-user viewing workstation). Never use lossy compression in the middle of your image-dispatching chain.

Noise and Compression

Noisier images, such as thin CT slices, are harder to compress losslessly – noise destroys pixel similarities (redundancy). Lossy compression, on the other hand, rounds pixel values to make them more similar and in many ways works like noise-reduction algorithms. This justifies the use of lossy compression for traditionally noisy modalities such as ultrasound.

6.2.3

Streaming Compression

Streaming (progressive), on-demand compression can be viewed as an intelligent extension to standard compression. Streaming is somewhat similar to the wellknown thumbnail images (the “click here to enlarge” kind), which you have seen so many times on the Web. Consider the following example: you need to download and review a large CR image. The resolution of your monitor is 1024 × 768 pixels, and typical CR image size is 2000 × 1500 pixels; that is, the CR image has roughly twice the width and height of your monitor. It does not matter how you zoom and pan the image, it will never fit into your viewing area with its original resolution. If your network is not fast enough, why bother trying to load the whole image all at once if you can never see it entirely? Streaming compression intelligently keeps track of the current image viewing area, adding high-resolution details only to those parts of the image you are currently viewing (Fig. 6.5). Because high-resolution details are the most difficult to compress, using them only when needed reduces cumulative image size and download time. Initially, streaming compression sends a user an image with the lowest possible detail level, just sufficient for the user’s monitor. Low detail means more similar, more redundant and therefore more “compressible” pixels, loaded much faster compared to the full-detail original. Then, as the user zooms and pans into particular image areas, the streaming compression loads additional, high-resolution image details for those areas only.

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First download

Original on PACS archive

Initially loaded low-resolution view Second download

Enhanced detail for selected viewing area

Fig. 6.5 Loading high-resolution image with streaming compression: first, the entire image in low resolution (small download size), then sharp local details on demand, depending on the viewing area selected by the user

This can substantially improve the image download speed because: 1. A user is often looking only at a certain image area and does not need to have a high-resolution copy of the entire image. Similarly, looking at a series of images, a user looks at only a few images at a time. 2. A user can start working with the image almost immediately, not waiting for the entire high-resolution image to download. 3. Breaking the entire image download into small portions of currently needed details makes download time less noticeable to the user. These features have made streaming compression popular with teleradiology systems, when it can provide additional (and often substantial) time gains for large image downloads. The only drawback of streaming compression comes from its main advantage: the distribution of the entire image download over the image

6.2

Image Compression

93

viewing time. When the user wants to browse between different areas of the image, navigating to each new area requires a new, high-resolution detail download. These small but repetitive downloads slow the navigation, and worse, they constantly alter the image appearance; the fuzzy, low-resolution parts become ever sharper as new details are loaded to them. This has become really annoying to many radiologists, and I have seen some who, after using the streaming compression systems, have returned to the standard non-streaming solutions. “Yes,” one of them said to me, “I know that I will have to wait a few minutes for a non-streaming image to download, but at least this happens only once at the beginning, and then I get the entire study to do whatever I want. Besides, I can switch to another task during this initial, lengthy non-streaming download – verify some reports, dictate something on the phone – while with streaming I have to waste my time in small unproductive increments.” Nevertheless, streaming image compression can be quite advantageous when the average image (study) size meets the network bandwidth. Always evaluate its potential benefits within your particular environment to determine whether you can use it. At the time this book was written, no streaming compression support was readily available in DICOM, but it could very well appear in the future. Certain static image compression standards, such as JPEG2000, support various compression levels for different image areas – provided someone can decide at the compression time which areas are more important and should be preserved in compression with the highest detail available.

6.2.4

Choosing the Right Compression Technique

Many current DICOM applications come with a colorful bouquet of supported image compression algorithms, and with a few simple clicks you can enable or disable any of them. Before you do so, you should carefully consider several important issues. First of all, you must choose between lossy and lossless compression techniques. If you produce original images for diagnostic readings, or if you store these images short time (disk space is not a problem), always pick lossless. This guarantees that the images remain unchanged. RLE, lossless JPEG, lossless JPEG2000, lossless JPEG-LS, and ZIP compression algorithms do not modify the images. If you do wet readings, if you run a teleradiology project (struggling with network speeds), or if you store images long term, you may as well opt for lossy compression. Certainly, you would usually have to do so only if your network bandwidth or your image archive size drag your business down. With lossy compression (such as lossy JPEG or JPEG2000), the rule of thumb is staying within a 10:1 compression ratio (Rcomp < 10) provided that you can control Rcomp in your software. In fact, one major drawback of DICOM compression support is the total lack of DICOM control over Rcomp. There is nothing in the DICOM standard to set/verify lossy compression ratio; you can choose only the compression algorithm. Therefore, support for Rcomp is always delegated to your DICOM software manufacturer, who might provide you with this option.

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How to Find Rcomp

If your software permits (and it usually should), save the same DICOM image on a disk, first with compression option turned on, and then with compression turned off. Then the ratio of the image file sizes, uncompressed to compressed, will be a good approximation to Rcomp.

Whatever compression you choose, never use vendor-proprietary compression for storage – you surely are not looking to create migration problems. Second, compression packs the data into a shorter format, but to be able to view it your software always has to unpack it. You can often hear something like “Smaller compressed image file size allows for faster image display.” This is totally wrong. Using image compression means two things: 1. Packing and unpacking compressed image data takes time in and of itself. It can perceptibly slow image display, and can be quite computationally extensive, even to the point of slowing down everything else running on your computer. Therefore, the advantage of gaining storage and increasing download times is always eclipsed by increased processing times. Usually, this is not an issue (and herein lies the reason you need a faster computer anyway), but some compression techniques (such as JPEG2000) are known to take much longer to compress/uncompress compared to others (such as JPEG-LS). 2. When images arrive at their destinations and need to be viewed, they will have to be uncompressed. This means that all (7FE0,0010) buffers return to their original data size – as large as they can be. Therefore, compression will not make your viewing workstations more efficient; they still need sufficient memory and hard disk space to handle the uncompressed data. The main beneficiaries of image compression are always your image archives (storage) and networks, but not the viewing workstations. This temporary nature of compression can be confusing. You could walk into your ultrasound room and hear users say something like: “Why does my computer work so slow? My ultrasound files are only about 30 MB in size!” Well, these image files are 30 MB because they are compressed with some 10:1 ratio. When they are loaded into computer memory for display, they are uncompressed to the original size of 300 MB – enough to start dragging the computer down. If the advantages of image compression for storage are obvious (you cut your storage size by the factor of Rcomp), making compression gains for networks is a bit trickier. Have a look at Fig. 6.6. Sending an image compressed with ratio Rcomp reduces network transmission times, but will require additional time to compress and uncompress the image. Therefore, if you want to use image compression to speed up your network transmission by K times, according to our graphic you will have to achieve: G = T / Tc = 1 / (1/Rcomp + 2N/C) > K, or C > 2N / (1/K – 1/Rcomp)

6.2

Image Compression

95

Network, speed N (bytes/sec), sending S bytes

Original image, size S (bytes)

Image at destination

Compress image with ratio Rcomp, compression speed C (bytes/sec) Network, sending S/ Rcomp bytes

Uncompress image with ratio Rcomp, compression speed C (bytes/sec)

Compression vs. network speed: Time to send uncompressed image: T = S/N seconds Time to send the same image, compressed with ratio Rcomp: Tc = (time to compress) + (network time) + (time to uncompress) = S/C + (S/ Rcomp)/N + S/C Compression gain factor: G = T/ Tc = 1/(1/ Rcomp +2N/C).

Fig. 6.6 Using image compression on networks

If we consider a moderate lossless compression ratio of Rcomp = 3 and we want to achieve a reasonable twofold transmission speed-up of K = 2 we get: C > 2N / (1/2 – 1/3) = 12N The image-compressing algorithm must process data at least 12 times faster than the network speed. Even if you have a relatively slow 10 Mbit/s network, you will need a 120 Mbit/s = 15 MByte/s compression algorithm to make your images travel twice as fast. Current compression algorithms, however, usually work on the 1–2 MByte/s level. As you can see, on reasonably fast networks they will actually slow you down rather than speed you up. On the other hand, in distributed clinical networks like those used for teleradiology, overall network speed tends to be low,3 and compression can noticeably improve image transmission rates.

3

Often due to the “last mile” problem.

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Third, compression ratio Rcomp always depends on the image and cannot be known before the image is compressed. For example, images with large monotone backgrounds (such as mammograms) generally compress better than images with substantial noise (such as nuclear medicine) simply because monotone translates to redundant and compressible. These days, increasingly higher performance is expected from 3D compression techniques – we can use 3D JPEG2000 as one good example. 3D compression explores the additional dimension of inter-image redundancy, which allows us to achieve higher compression ratios. Also, each compression algorithm has its own class of medical images for which it will perform best. For example, 8-bit lossy JPEG, completely inappropriate for CT or MR images,4 works great with ultrasound and is commonly implemented on ultrasound devices. Finally, although the images account for the bulk of DICOM data, TCP/IP networking in general and DICOM networking in particular work in their own ways, taking additional time to establish connections, communicate protocols, and to break and assemble data into smaller packages. Therefore, even with the packing and unpacking time excluded, do not expect your network to transmit the compressed image exactly Rcomp times faster – some aspects will surely require more time. We have to leave the in-depth compression discussion for another time because it is beyond the scope of this book; but if you have the time, I suggest you do a little reading on modality-specific compression ratios (Branstetter 2009), and consult with an expert before implementing any particular image compression type at your medical enterprise.

Low-Hanging Fruits

Sometimes compression can be much easier to implement than it sounds. For example, Microsoft Windows supports built-in file compression – you can always right-click on a folder and set it as compressed. This means that all files, stored in this folder will be compressed with lossless, native Windows compression, regardless of what program you use to handle these files. What if this folder happens to be your image storage folder? Then all images, stored in this folder will be compressed automatically and losslessly, regardless of your archiving software. We once tried to use this method for our CT image archive, and it gave us an average Rcomp = 1.6. Not the most impressive result, but hey, we reduced image storage by 1.6 times by doing absolutely nothing! With the 10–20 cents/MB that many current PACS charge for image storage, you can buy several new imaging servers if you only compress data on one of them.

4

8-bit JPEG can handle only 8 bits per pixel, and CT or MR images have up to 16. The last thing you want to lose is the color/grayscale depth of the image. Always favor the compression algorithms that do not have any color depth limits.

6.3

Working with Digital Medical Images

6.3

Working with Digital Medical Images

6.3.1

Image Interpolation

97

With so many legal and clinical arguments spent on lossy image compression, the entire subject of image interpolation (digital zoom) seems to be totally ignored. Some time ago I wrote a program to export DICOM images into conventional image formats such as JPEG and BMP. Many radiologists still like to do this if not for medical purposes (which I strongly discourage), but for other work such as writing research papers and keeping teaching files. Soon after, one of my colleagues (an experienced radiologist) called me to ask why her MR images had become so small when exported – much smaller than she used to see on her PACS workstation. What was wrong? The answer was “Absolutely nothing.” When dealing with digital imaging data, what you see is more likely not what you have. A standard 256 × 256 MR image does look small on a typical 1024 × 1024 monitor, and looks minuscule on a good radiological one. For this reason, all PACS application do something that other imaging programs do not: they always interpolate the small images to make them larger so they can take advantage of the entire monitor area. Interpolation (digital zoom) is the process of changing the original resolution of a digital image, artificially increasing its pixel count. If you have a 256 × 256 image you have only 256 × 256 = 65536 pixels. If you want to show this image full screen on a 1024 × 1024 monitor, it must be converted to a 1024 × 1024 image with 1024 × 1024 = 1048576 pixels. So, when you zoom this image to full screen, where do the extra 1048576 − 65536 = 983040 pixels come from? They are generated by an interpolation algorithm5 and inserted between the original image pixels, thus making the image matrix larger. As a result, when you zoom in on a digital image, you see progressively more pixels that were not present in the original data but are added by the interpolation. Thus in a 4X-zoomed image, only one pixel out of every 4 × 4 = 16 pixels comes from the original image, and the other 15 are essentially created by a program (Fig. 6.7). A substantial addition, isn’t it? Much more substantial than any reasonable lossy compression would do. This dominance of the artificial pixels explains why image interpolation should be taken very seriously by all PACS developers and users, and a good amount of research continues in this area. Because the interpolating program has no access to the original object (patient) to make a better image, it has to cleverly fake all the extra pixels so that they look as natural as possible. The quality of this faking should be extraordinary and can always be used to judge the quality of image viewing software. To test a PACS viewing workstation that you might consider buying, load a small image (such as MR or NM), zoom in on it and check for tiles, jaggies, broken lines, and other unnaturally looking artifacts. If you see any of these problems, they

5

Bicubic interpolation is usually used.

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Fig. 6.7 4X interpolation. T stands for original (true) pixels, and I stand for the interpolated T

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T

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…

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Original image

Interpolated image

5X

Fig. 6.8 (Top) The original small nuclear medicine image. (Left) Poor nearest neighbor image interpolation. (Right) Much better bicubic interpolation. The snapshots were taken from two different commercial image viewing programs

are most definitely coming from poor interpolation, and you are looking at a very cheap approximation of the required standard quality. Figure 6.8 demonstrates this difference for a small nuclear medicine image, and Fig. 6.9 gives a more subtle example. As you can see, the interpolation on the left is extremely poor and the one on the right is much more natural. Interpolation does have another artifact that is impossible to avoid. When an image is zoomed in too much (such as 5X in Fig. 6.8) you will start seeing some blur. This merely reflects the fact that interpolation does not add information to the image and cannot add any finer, sharper details. Hence another conclusion: do not spend your money on an expensive, super-high resolution monitor if you mainly deal with

6.3

Working with Digital Medical Images

99

Fig. 6.9 Same image opened in two different PACS apps. Note severe aliasing artifacts (“staircasing”) on the left image, displayed with inferior interpolation algorithm

small images. Typical nuclear medicine (NM) images can be as small as 64 pixels (in width or height); MR image width and height would be either 256 or 512 pixels; CT images 512 pixels; and US images 400 or 800 pixels. Any conventional monitor these days can handle these numbers. Do not pay more for fake zoom. Digital interpolation checks should become simple and efficient tricks you use to tell high-quality imaging software from flashy imposters. This is particularly true if you can compare images from two programs side-by-side, as shown in Fig. 6.9. Human eyes are great at spotting unnatural digital artifacts, and you do not have to be a radiologist to see them; besides, what strikes you at first sight will definitely annoy radiologists in their routine work. Find some sharp intensity changes (such as bright edges on a dark background) and zoom into them. High-quality interpolation should produce naturally smooth shading, and must not distort the original shapes of the objects. Surprisingly enough, DICOM – usually very meticulous about image and display quality – bypasses the entire subject of digital image interpolation.

License to Zoom

I am always amused by the spy movies, carelessly abusing the fine art of digital interpolation. A brave detective looks through lame security camera videos, trying to find his evil suspect. “Stop!” he yells violently, “Stop this frame! See the wrist watch on this guy’s hand? Zoom in! Now, see this pale reflection on the watch dial? Zoom in! Recognize this face in the pale reflection on the wrist watch dial?” Unfortunately, this does not work in real life – do not confuse your PACS monitor with the Hubble telescope. Once the image is taken, it is taken, and it has as much information as the number of pixels within. Interpolation will make the image bigger, but it will never make it more informative. And bad interpolation, as we have seen, will make it only worse.

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Fig. 6.10 3D image coordinates in DICOM

Medical Images in DICOM

z

Ip

vc

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vr x Dp

6.3.2

Image Reconstructions

Though I have mentioned so many times that DICOM is essential in diagnostic image processing, I’d like to show you an example of how it makes everything fit into place. Image reconstructions – planar, curved, surface, 3D – have become vital in modern radiology. They are also one of the most distinctive advantages of digital imaging over the old hard-copy film (when you couldn’t post-process anything). No reconstruction post-processing would be possible without the information collected in DICOM data objects. Consider a simple digital image, such as a CT scan. In addition to its pixel attributes, DICOM records all associated distances, 3D coordinates, and orientations. In particular, as shown in Fig. 6.10: • Spacing between image pixels, Dp, stored in DICOM (0028,0030) “Pixel Spacing” attribute. It defines the physical size of the image pixel, and makes possible any real distance measurements. For example, if you know that pixel spacing in the x and y axes is 0.4 mm, then a 10-pixel image line will correspond to 4 mm distance. Also, because you know the image width and height in pixels (such as 512 × 512 for an average CT), you can find the physical image sizes (field of view): 512 × 0.4 mm = 204.8 mm. • “Image Position” (0020,0032) attribute, Ip. These are the x, y, and z coordinates of the upper left hand corner (first pixel) of the image, in mm. This is how we know where the image begins in 3D space.

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• “Image Orientation” (0020,0037) attribute. It contains the 3D direction cosines of image row and column vectors vc and vr. These two vectors, originating from Image Position point Ip, completely define the entire image plane in 3D space. Now, if we have an image pixel P in row r and column c, we can find its 3D coordinate as P3D = Ip + r × vr + c × vc, computed in x, y, and z coordinates. • “Spacing Between Slices” (0018, 0088) attribute, Ds, records the distance between consecutive image slices, in mm. It serves the same purpose as (0028, 0030) “Pixel Spacing,” but in the z direction. For example, if you consider the same pixel (r, c) position on the first and the second CT slice, then the distance between these two will be equal to the (0018, 0088) attribute value. • Image time, thickness, location and a few more DICOM attributes record the other important coordinates. For example, image acquisition time becomes essential in perfusion analysis, when we need to process the information from the temporal image series. Now imagine that you have an original CT series, and you want to create an oblique image, slicing the same series data in a different image plane. Thanks to DICOM attributes, you will be able to achieve this in a few straightforward steps: • Define new image plane attribute for your oblique section: image position point Ipo, and two image orientation vectors, vro and vco. Spacing between pixels in the oblique plane should more likely remain the same as in the original image, to maintain the same aspect ratio. For the same reason assume that the image size in pixels also remains the same. • Compute the value for each image pixel in your new oblique image plane as follows: 1. Take each pixel coordinate in the oblique plane, (ro, co). 2. Convert it to the global 3D space according to our earlier formula: P3Do = Ipo + ro × vro + co × vco, This will tell you where in 3D space the new P3Do pixel is located with respect to the pixels of the original image series. 3. Using the original image series, find value in P3Do point with interpolation. • Assign this value to the oblique image pixel, and proceed to the next one. The interpolation step 3 above is done with the same technique as we studied a bit earlier. When we compute the 3D coordinates of the current oblique image pixel P3Do, this pixel will fall somewhere in between the original series pixels. The simplest nearest neighbor interpolation may just find the closest to P3Do pixel in the original series, and assign its luminance to the oblique pixel. Better linear interpolation will use the eight closest original pixels to linearly interpolate the luminance in P3Do. This same approach with minor modifications is used for oblique (multiplanar), volume, maximum intensity and minimum intensity rendering types (Fig. 6.11). In all cases, the most important step is to compute intensity value at a given 3D point P3Do, and with a few DICOM attributes this becomes absolutely possible. As you might imagine, with a large number of pixels to reconstruct, processing 3D volumes takes considerable computational effort and time, which is why it is

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z

y

x

Fig. 6.11 Using DICOM for 3D reconstructions from the original image series

always performed on special hardware accelerators built into high-end graphics cards. The digital circuits of these accelerators are designed to do things like interpolation on the fly. This is what you are paying for when you purchase a 3D rendering workstation or server. Interestingly enough, the entire rendering methodology – hardware included – came to radiology from the computer gaming industry where it was originally perfected many years ago. However, even the best technology clearly would not do much without DICOM attributes. DICOM provides all essential information for the most elaborate image reconstructions, from simple oblique slices to the most intricate “Virtual Navigator” software.

6.3.3

Grayscale Depth

The question of “How many grayscale shades should my monitor support?” naturally complements the question of image spatial resolution and interpolation – except now we are talking about the resolution in the color/grayscale domain.6 Many aspects of this discussion are often mixed, making it very confusing; so it is worth spending a couple of minutes to put things in order. What really matters is the original image color depth. For instance, consider a CT image with bits stored (Bs) equal to 10. Then the image contains 2Bs = 1024 shades of gray, as you now know from Sect. 6.1. As long as the image is kept in this 6

As we have previously agreed, by image color depth we mean the number of image shades, whether they are grayscale or color.

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Fig. 6.12 Same CT image, shown at different window/level settings: overall view (left), lungs (middle), soft tissue (right)

original DICOM format, it will keep all its 1024 shades, so they can be displayed and viewed with proper software or monitors. On the other hand, as soon as the image is exported into a non-DICOM format such as BMP or JPEG, supporting only 256 shades of gray, or compressed with the wrong compression technique, there is no way to preserve the original 1024 gray levels. No software or hardware in the whole wide world will display a degraded image with the original quality: what is lost is lost (same story as with overused lossy compression). This is the principal reason why any teleradiology project (often falling for simple image formats and exports) should maintain the original image format rather than try to “recover” permanently lost quality with overpriced viewing workstations. This is also the reason why many official radiology project guidelines (including those for teleradiology) insist on preserving the original image quality more than mandating image viewing options7 (TeleradUSA). Preserving diagnostic image quality is a must. Everything else is simply subjective; mattering only for personal viewing comfort. Consider the window/level (W/L) function – available in absolutely all PACS or DICOM software – that is used to adjust the currently visible grayscale range. For example, if you are viewing a CT image with 1024 shades of gray on a conventional off-theshelf display (capable of 256 grayscales only), you still will be able to navigate in the original 1024 range with your “bone,” “liver,” “brain,” and other windows (Fig. 6.12). Each of these windows would simply take the corresponding [C0, C1] subrange from the original 1024 CT shades and map it into the 256 available to you on the monitor. In other words, this is very similar to zooming and panning the image, but now you “zoom” (window) and “pan” (level) in the grayscale range. If the original image quality was preserved, you would be able to see all image shades on any monitor – ranging from your cell phone display to your radiological 10-megapixel monster. Simply put, on a 1024-bit radiological monitor you will see them at once, and on a 256-bit conventional monitor you will have to navigate between them. 7

The only exception is made for mammography displays: they are expected to have impressive 5-megapixel resolution (typically 2,048 × 2,560 pixels), and current models deliver high 750 cd/ m2 luminance with 4,096 supported grayscales.

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1000 Monitor luminance, cd/m2

500

100

10

1

0.1 0

200

400

600

700

800

1000

JND

Fig. 6.13 JND curve. Note that the luminance axis is logarithmic

With all this being said, we can only debate the affects of different monitor properties on the optimal image grayscale display. By far, the most important properties would be monitor luminance and contrast ratio. Contrast ratio is the ratio between the brightest white and the darkest black that the monitor can reproduce. For example, a contrast ratio of 1000:1 means that the brightest monitor shade is 1000 times brighter than the darkest. Monitor luminance, typically expressed in nits (candelas per square meter, cd/m2), measures the amount of light produced by the monitor. Naturally enough, higher contrast and luminance come together and mean better differentiation between the image shades, which translates into better image interpretation quality.8 DICOM describes this differentiation with the concept of Just Noticeable Difference (JND) – the smallest difference in grayscale that the average human observer can perceive under given viewing conditions. Theoretically, JND depends on monitor luminance – you can find a really impressive empirical formula in DICOM PS3.14, corresponding to the curve shown in Fig. 6.13. For example, if you pick a pretty good 500 cd/m2 luminance monitor, your eyes would be expected to see some 700 different shades of gray – provided, the monitor and its graphics card are capable of supporting those 700 shades (see Annex B in DICOM PS3.14 for a more complete JND value table). Moreover, if the monitor can display 1024 shades, you still won’t perceive more then 700 at 500 cd/m2. Apart from these averaged and theoretical expectations, the JND function is used for one

8

Because of this, luminance and contrast numbers turned into pure sales pitches with many display manufacturers – sometimes claiming much higher values than they can actually deliver. It is always worth the time to evaluate the monitor display quality visually, preferably with side-by-side monitor comparison.

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very practical purpose: calibrating radiological monitors. The entire purpose of calibration is to tune monitor luminance at different levels in such a way that JNDs would be uniformly distributed. Your ability to differentiate between darker shades will be the same as your ability to differentiate between the lighter ones. That provides the sense of not favoring certain intensity ranges at the expense of others – for example, in the CT case, not making Hounsfield units more visible in one density range compared to the others. The rest of the perfect monitor debate gets extremely vague and subjective – especially when we get into the area of diagnostic display quality. Even the most perfect JND calibration cannot guarantee optimal viewing – the human vision system is very complex. For example, your ability to perceive certain shades will be affected by image noise, distribution, shape, and position of the objects you are looking at. Your perception is also affected by the ambient light and by a host of other factors – possibly including the height of your chair and the quality of your diet. In some hospitals, reading rooms are not just dark, they are painted black, and radiologists cannot wear bright coats while working there. Finally, experience shows that mere image reading comfort sometimes means much more to quality image interpretation than the most technologically advanced solutions. For example, seeing the maximum number of grayscales at once can still make image reading easier (Kimpe and Tuytschaever 2007), but it also translates into increased monitor luminance (as shown on the JND curve in Fig. 6.13). According to some reports, this can become a distraction in itself if above 600 cd/m2. But 600 cd/m2 correspond to only 730 JNDs – well below our 1024 shades in an average CT or X-ray image; and 1024 JNDs would require blasting 4000 cd/m2, which is quite likely unbearable to look at. As a result, current 300–400 cd/m2 radiological monitors provide comfortable luminance, but do not get anywhere near the original image grayscale depth. This analysis sets limits to the clear-cut JND theory, which is why more and more institutions try to solve the display “grayscale depth” puzzle from the empirical point of view. How many monitor shades would be enough for a radiologist to read images with maximum comfort and minimum errors? In one experiment Bender et al. 2011, the images with low-contrast pathologies (small lung nodules, little bone fractures, low-contrast tumors, and such) were presented to radiologists with conventional 8-bit and advanced 10/11-bit technology. The radiologists were asked to select the best image quality, and the results were quite surprising: in the majority of cases, the radiologists did not appreciate the deeper 10/11-bit grayscale and were as productive and accurate with the usual 8-bit displays. Moreover, in some cases the deep 11-bit monitor grayscale was perceived as “less sharp”; the higher number of shades was creating an illusion of a visibly smoother image, which was perceived as degraded, blurred quality (Fig. 6.14). How ironic! Humans are amazing creatures. You can never tell what will make them happy; and sometimes the simplest things work best. Bottom line: The original DICOM image quality is all that matters. It was meant to be diagnostic, so let’s keep it that way. I am typing this book using a new, off-theshelf monitor that has 500 cd/m2 brightness, 1000:1 contrast ratio, and 1200 × 1900 pixel resolution – nearly the same as or even better than many current PACS monitors produce. The real difference is that the PACS monitors are ten times more expensive (Hirschorn).

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Monitor Placebo

A most interesting case of “expected vs. practical” monitor use happened to me a couple of years ago in a well-recognized international hospital using PACS from a well-respected PACS vendor. Because the PACS came equipped with high-resolution, dual-monitor radiological displays (from a wellrespected display manufacturer), everyone was absolutely certain that they were 10 bits per pixels, thus capable of 210 = 1024 simultaneous grayscales. Moreover, the PACS company confirmed that their PACS software was also 10-bit capable. After 2 years, by sheer accident we discovered that the monitors were only eight bits per pixel – in other words, providing only of 28 = 256 grayscales, which any conventional monitor would deliver! It just turned out that although the monitors and the PACS software were 10-bit compatible, the graphics cards in the PACS workstations supported only eight bits, thus converting everything to the 8-bit display mode! As a result, the hospital radiologists kept doing their top-notch job on very basic monitor setups simply because they believed that their monitors could do much more. An interesting case of viewing subjectivity and PACS vendor incompetence at the same time.

100 8-bit better Same

90

11-bit better 80 70 60 50 40 30 20 10 0 Sharpness

Contrast

Detail detectbility

Overall performance

Fig. 6.14 Comparing 8-bit and 11-bit image display in (Bender et al. 2011) study: most radiologists did not see any difference, and many favored 8-bit monitors as providing sharper image display

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Signal amplitude Sampling step dt

Sampling frequency f = (1 second)/dt

Time

Fig. 6.15 Digitizing continuous (analog) signal into a sequence of discrete (digital) values

6.3.4

Waveforms

Waveforms are not images, but are close in many ways. As DICOM states: The Waveform information entity (IE) represents a multi-channel, time-based digitized waveform. The waveform consists of measurements of some physical qualities (for example, electrical voltage, pressure, gas concentration, or sound) sampled at constant time intervals. The measured qualities may originate, for example, in any of the following sources: • The anatomy of the patient, • Therapeutic equipment (for example, a cardiac pacing signal or a radio frequency ablation signal), • Equipment for diagnostic synchronization (for example, a clock or timing signal used between distinct devices), • The physician’s voice (for example, a dictated report).

Digital sound is by far the most common waveform that we all know even before we get into any digital medicine business. ECG is another popular example. Arterial pulse and respiratory waveforms were added to DICOM last year. In each case, waveforms capture processes that evolve in time and can be represented with several signal channels (stereo sound, for example). In this respect, waveforms are very similar to images. But whereas images are characterized by two spatial coordinates (x and y), waveforms have one-dimensional amplitude changing in time t (see waveform screenshots on Fig. 12.3, left). Consequently, just like images, analog waveforms must be converted to digital equivalents. This means that an analog signal is broken into discrete samples that are recorded as their amplitude values, thus forming a digitized waveform (Fig. 6.15).

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Once digitized, this becomes a digital sequence of samples (amplitude values) that can be saved in DICOM the same way image pixels are saved. The terminology of bits allocated and bits stored applies to waveform representations. In practice, DICOM waveforms are still rarely used compared to digital images, and are very poorly supported by DICOM vendors. Nevertheless, waveforms represent an excellent tool for storing one-dimensional, multichannel sequences; whatever they represent. If you are interested in learning more about waveform formats, check DICOM section A.34 in PS 3.3, and Supplement 30. As DICOM continues to embrace digital multimedia, the role of waveforms may only grow.

6.3.5

Overlays

Overlays (or overlay planes) are images that can be used to record graphics and bitmapped text (such as annotations) on top of the regular DICOM images. This makes overlays very useful and widespread, although the long history of varying overlay formats in DICOM has contributed to poor overlay implementations. Why do we need image overlays if we have images? Here is a simple use case: you want to draw an annotation on top of your CT image, tracing a tumor or some region of interest, and adding some measurement text. How would you do this? The first thing that comes into mind is “burning” (or “etching”) the annotation into the image pixels – that is, making it a part of the original image. This is definitely the easiest approach; but unfortunately it is also the worst, and it won’t work in most cases. First of all, it will destroy the original image data. Second, it will destroy the original image format. If you want to put a red circle on a monochrome CT image, you will have to convert the entire image from CT to SC screenshot; bye-bye, Hounsfield calibration! (Fig. 6.16). Therefore, we need another image layer – that is, an overlay – to separately store additional graphics, and DICOM got busy with overlays quite a while ago. The original thought was to keep overlay pixels along with the original image pixel values in the (7FE0,0010) tag, as we discussed in 6.1. For example, if a CT image pixel usually takes 12 bits (“Bits stored”), it is rounded to 2 bytes = 16 bits (“bits allocated”) of pixel memory; and we have 16 − 12 = 4 unused bits to store something else such as overlay bits. This is what early DICOM used to practice; making a “pixel burger” with overlay bits on top. That approach was truly awful for many reasons, as you can easily imagine: different data always deserves separate storage. So the “burger” approach was eventually abandoned, and overlays (or “curves” before 2004 DICOM edition) were repackaged into separate DICOM elements: repeating groups9 60XX (Table 6.3). The joy of moving overlay pixels from (7FE0,0010) to 60XX was spoiled by a few artificial limitations imposed by ever-creative DICOM authors. First of all,

9

Repeating groups like 60XX are the leftovers of the pre-DICOM standards, which you can still find in a couple of places. They simply mean that XX can take different values in some range. DICOM wants to retire them eventually, using SQ sequences instead.

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Fig. 6.16 Placing overlay (bitmapped text in this example) over CT image

overlays were reduced to only 1 bit/pixel “black-and-white” format. This left many users unhappy – grayscale or color overlays would be very appreciated in many cases. To make things worse, DICOM limited the span for hexadecimal XX from possible 00 to FF (decimal 0 to 255) to the even-numbered XX in the 00-1E range (decimal 0 to 30), which as you can easily count, produced only 16 possible group numbers. As a result, you can have at most 16 overlays per image, each in 1 bit/pixel format. In theory, you can use them all – for example, displaying each in different color – but “there is no mechanism to specify with what color or intensity an overlay plane is to be displayed10,” which means that your overlay coloring will be application-specific. As a result, nearly all DICOM companies default to a single overlay plane stored in the 6000 group (XX = 00) as the most supported and cross-compatible solution. But even this should be regarded as a great step forward: many DICOM vendors, confused by the complex overlay history, do not support overlays at all, storing annotation and ROI data in their proprietary formats. No wonder that when their images are transferred to other systems, their overlays miraculously disappear from the PACS monitors as if they were written with invisible ink.

10

See section C.9 in DICOM PS3.3.

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6

Table 6.3 Overlay plane attributes Attribute name Tag Overlay Rows (60xx,0010)

Type 1

Overlay Columns

(60xx,0011)

1

Overlay Type

(60xx,0040)

1

Overlay Origin

(60xx,0050)

1

Overlay Bits Allocated

(60xx,0100)

1

Overlay Bit Position Overlay Data

(60xx,0102) (60xx,3000)

1 1

Overlay Description

(60xx,0022)

3

Overlay Subtype

(60xx,0045)

3

Overlay Label

(60xx,1500)

3

ROI Area ROI Mean ROI Standard Deviation

(60xx,1301) (60xx,1302) (60xx,1303)

3 3 3

Medical Images in DICOM

Attribute description Number of Rows in Overlay (usually the same as image number of rows, that is, image height) Number of Columns in Overlay (usually the same as image number of columns, that is, image width) Indicates whether this overlay represents a region of interest or other graphics. Enumerated values: G = Graphics R = ROI Location of first overlay point with respect to pixels in the image, given as row\column. The upper left pixel of the image has the coordinate 1\1 Column values greater than 1 indicate the overlay plane origin is to the right of the image origin. Row values greater than 1 indicate the overlay plane origin is below the image origin. Values less than 1 indicate the overlay plane origin is above or to the left of the image origin. Values of 0\0 indicate that the overlay pixels start 1 row above and one column to the left of the image pixels Number of Bits Allocated in the Overlay The value of this Attribute shall be 1 The value of this Attribute shall be 0 Overlay pixel data, one bit per pixel The order of pixels sent for each overlay is left to right, top to bottom, that is, the upper left pixel is sent first followed by the remainder of the first row, followed by the first pixel of the 2nd row, then the remainder of the 2nd row and so on Overlay data shall be contained in this Attribute User-defined comments about the overlay USER (if created by user), or AUTOMATED (if created by software algorithm) A user defined text string which may be used to label or name this overlay Number of pixels in ROI area ROI Mean ROI standard deviation

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Therefore, please make sure that your DICOM software can handle overlay planes: just find some DICOM images with group 6000 and open them in your program – you should see the overlay. If you use overlays for annotations, keep in mind that any graphics or text you put in the overlay will be bitmapped – converted to overlay black-and-white pixels. It limits overlay use: once your text and graphics are bitmapped, you cannot edit them anymore. But it also makes overlays the simplest images on earth, impossible to get wrong.

6.3.6

Supporting True Video in DICOM

Want to store your favorite video in DICOM, and play it on PACS? Why not? Just imagine “House M.D.” fans watching favorite episodes in the radiology department, straight from the PACS archive. I am almost tempted to start writing a business plan for a “PACS TV” startup. But before I digress into some hare-brained scheme for becoming an overnight Hollywood sensation, let’s reel things back in and have a look at the basics. The original DICOM had no support for digital video; all images were expected to be still frames. The need for the DICOM video, however, was soon realized with the earliest video modality: ultrasound. In ultrasound imaging, short video clips – called “cine loops” – are commonly used to record beating hearts, rolling babies, and blood flow to name just a few. Most of us have likely had some experience with them at least once in our lives. To accommodate these clips, DICOM applied the same idea that the first film inventors used from the early cinematographic times: a video is nothing more than a running sequence of still images. In other words, DICOM slightly extended its single image format to support multiframe images, storing each video as a sequence of its frames (still images as usual) packed into a single DICOM object (file).11 Video parameters such as Frame Time (0018,1063) or Cine Rate (0018,0040) were recorded along with them, to indicate video format and to ensure correct playback. Each DICOM workstation was expected to play the stored video accordingly; that is, to automatically scroll through the stored sequence of still images at a specified playback rate. To keep the data size small, lossy JPEG compression was applied to each stored frame (Fig. 6.17). This “cinematic” approach worked for years, and still works pretty well for ultrasound images in particular. In fact, ultrasound imaging proved to be the ideal domain for the multiframe DICOM. Short cine loops (small numbers of video frames), inherently low ultrasound image resolution (ideally matching lossy JPEG compression), no need for audio, and the ability to extract and process individual image frames all transformed multiframe ultrasound images into a widely accepted and practically convenient standard, later adopted by the other video modalities. The only practical problem I ever ran into with multiframe images was their loading

11

All frames are concatenated into the pixel buffer, (7FE0, 0010) attribute.

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Fig. 6.17 Displaying multiple frames from DICOM multiframe image. The video – all 409 frames in this case – was stored in a single DICOM multiframe object, as a sequence of 409 JPEGcompressed color images, 480 × 640 frame size. Thanks to JPEG, the DICOM object size was reduced to 6 MB instead of the original 409 × 480 × 640 × 3 (color bytes) = 360 MB. Note that 60:1 JPEG compression had no negative effect on the naturally shaded image with a large black background

time: each image frame needs to be extracted from the data stream and uncompressed, thus taking substantial time and memory. But the ever-improving processing power of computers was able to keep this at bay. During the past few years, however, the landscape of true video applications in DICOM has started to change rapidly. “Visible light” modalities (VL) – such as endoscopes, microscopes, and even plain video cameras – came into play, making their mark in DICOM (see Visible light IOD in PS 3.3). Users got really busy producing long-lasting videos with resolution and quality often surpassing that of good old ultrasound. Audio has become important to record physiological sounds and physician comments. The old multiframe format, although still applicable, soon started to reach its limits. The need for video-specific formats such as MPEG2 has become apparent. We all are pretty accustomed to MPEG video in our everyday lives – playing it on iPods, digital cameras, and computers. MPEG does not cancel the fundamental “frames per second” principle; it still stores digital video as a sequence of frames. But instead of compressing and storing each frame independently, MPEG compresses and stores only the difference between the two consecutive frames, substantially minimizing the data volume: in most cases, only a few spots on the next video frame change, while the background remains largely the same and does not need to be encoded in each frame. Combined with more superior compression, MPEG definitely surpasses the quality of simple-minded multiframe images. To make it work in digital medicine, the problem of MPEG support was solved in the typical DICOM way – by encapsulating MPEG2 video data into DICOM

6.3

Working with Digital Medical Images

Table 6.4 MPEG2 MP@ML video format attributes Video type Spatial Frame Frame resolution rate time (ms) 525-line NTSC Full 30 33.33 625-line PAL Full 25 40.0

113

Maximum rows 480 576

Maximum columns 720 720

objects. That is, MPEG-encoded video can be placed into the Pixel Data (7FE0,0010) attribute – the same attribute where still images used to be stored. In this way, DICOM support for digital video did not result in any new object types or definitions. Just like JPEG and PDF, MPEG has become yet another data (compression) format adopted by DICOM objects. First of all, this makes MPEG format negotiable at Association Establishment handshake; if it is refused, the video-sending device needs to use the default uncompressed multiframe image format.12 Secondly, the use of MPEG is totally optional: one can still store long endoscopic video as a traditional DICOM multiframe. Moreover, this is still the most used approach. But when MPEG is used, the video will be stored in MPEG2 format, and its presence in the DICOM object will be indicated by MPEG2 transfer syntax – 1.2.840.10008.1.2.4.100 or 1.2.840.10008.1.2.4.101 (more on transfer syntaxes in 9.4). Why two different MPEGs? The “MPEG2 Main Profile @ Main Level” was the one introduced originally. This is the same format used in most digital devices and videos, DVDs included. MPEG2 MP@ML supports the video types shown in Table 6.4 (DICOM PS3.5 and MPEG2-dedicated Supplement 137). Video is always stored in RGB format (8 bytes per color channel, three color channels13), compressed with inherently lossy MPEG compression. Each DICOM object can contain a single video clip, limited by the 2 GB size (there are no “miltivideo” DICOMs yet). If the video does not fit into this size, it has to be cut into several fragments, each stored in its own DICOM object. Audio is supported just like with any video in general, being a part of the MPEG data stream. In 2009, high-definition MPEG – “MPEG2 Main Profile @ High Level” – was added to support HD video. As you can see from the format tables (Tables 6.5 and 6.6), the resolution and frame rates of the original “MPEG2 Main Profile @ Main Level” doubled in the newly added HD version. Is it really needed? Only time will tell. Currently, the DICOM adaption of video codecs, like MPEG, was driven mainly by the need to store the videos from the VL modalities – digital video cameras of various kinds. Most of them recently went into the full HD video support, and DICOM merely followed this trend – no questions asked. On one hand, this opened the DICOM standard to the entire world of digital

12

In theory. I wonder if video camera manufacturers ever considered providing this function. This makes it inapplicable to other types of medical images, such as 2-byte/pixel grayscale in CT, MR, or X-ray.

13

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6

Table 6.5 MPEG2 MP@HL video format attributes Video type (Hz HD) Spatial resolution layer 30 Single level, enhancement 25 Single level, enhancement 60 Single level, enhancement 50 Single level, enhancement

Table 6.6 Rows 1080 1080 1080 1080 720 720 720 720

Frame rate 30 25 60 50

MPEG2 MP@HL video frame sizes and rates Columns Frame rate Video type (Hz HD) 1920 25 25 1920 29.97, 30 30 1920 25 25 1920 29.97, 30 30 1280 25 25 1280 29.97, 30 30 1280 50 50 1280 59.94, 60 60

Medical Images in DICOM

Frame time = 1 s/rate (ms) 33.33 40.0 16.17 20.00

Progressive or interlace P P I I P P P P

video. On the other, I have not seen a single major PACS workstation or hospital using DICOM MPEGs on a regular basis; the old multiframe image format remains dominant for the practical clinical needs. The use of MPEG-like video encoding calls for new image display, analysis, and processing tools; and they are not yet readily available. In fact, following the same trend of being driven by the emergence of HD medical camera products, DICOM Supplement 149 (released in 2009 from the DICOM Visible Light workgroup) introduced into DICOM the MPEG2 successor: MPEG4, the “blue-ray” video format. And although we all know the advantages of blue-ray videos, their use in PACS has yet to be proven practically. To conclude, it is worth mentioning that some older digital modalities relying on video display still lack adequate video storage. Let us consider digital angiography (DA) as one good example. DA involves sequences of grayscale X-ray images commonly taken to visualize blood flow. Unlike ultrasounds, these images have substantially higher resolution (averaging at 1000 × 1000 pixels), and 2-byte/pixel grayscale (unsupported by MPEG or “conventional” lossy JPEG). As a result, DA sequences cannot be efficiently encoded, and are still stored as multiframe DICOM, often with no compression of any kind. They produce DICOM objects of monstrous sizes, rivaling Hollywood epics (and I mean on the order of several gigabytes). From the users’ perspectives, they are still video, and questions like “Why does my DA take so long to load, and forever to play?” are common – the users simply do not realize the scale of the data size.

Part III DICOM Communications

7

DICOM SOPs: Basic

Anything complex is useless, anything useful is simple – Mikhail Kalashnikov

Remember what the “C” in DICOM and PACS stands for? Communications of course; and DICOM networking is the glue that holds any medical imaging system together. While most of us used to think of DICOM as a simple medical image file format, it really is a much broader standard that directs all facets of the clinical workflow and goes far beyond the scope of managing formats for image files. The entire digital medical universe is created and populated by DICOM objects as they travel and interact through computer networks. Interestingly enough, DICOM networking had been laid out in the standard well before computer networks came into existence. Part PS3.9 of the standard (Point-toPoint Communication Support for Message Exchange) used old-fashioned pin cables to interconnect DICOM devices. All this, including PS3.9 itself, has vanished with the introduction of modern networking hardware and protocols, which have become the foundation for contemporary DICOM data exchange. In plain words, current DICOM uses the exact same underlying network protocol – Transmission Control Protocol (TCP) and the Internet Protocol (IP) – that you use for sending your email or watching online videos (Fig. 7.1). TCP/IP nicely accommodates all hardware and software variations, and delivers the most fundamental network functionality needed: sending information (as a sequence of bytes) from one port/IP address to another. DICOM only adds its own networking language (application layer), which we are about to explore. This language consists of high-level services (DICOM DIMSE, the subject of this chapter) built on low-level DICOM association primitives (DICOM Upper Layer protocol, a more involved discussion reserved for Chap. 9). Although sometimes technical, all concepts used in DICOM networking are quite intuitive – you really do not have to be an IT guru to understand them. Moreover, knowing the principles of DICOM networking will considerably improve your understanding of DICOM and PACS, and your ability to deal with related projects. O.S. Pianykh, Digital Imaging and Communications in Medicine (DICOM ), DOI 10.1007/978-3-642-10850-1_7, © Springer-Verlag Berlin Heidelberg 2012

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DICOM Software Application

DICOM SOPs: Basic

Email software Web browser FTP client

Email (SMTP) High-level DICOM: DIMSE and SOP Web (HTTP) Low-level DICOM: association negotiation and PDU. Also known as DICOM Upper Layer protocol

File transfer (FTP)

Network application layer

DICOM networking:

TCP/IP networking: basic standard for modern computer networks

Fig. 7.1 DICOM and popular networking protocols. DICOM network application layer augments basic TCP/IP functionality with DICOM-specific protocols

7.1

Identifying Units on the DICOM Network

Each device residing on a TCP/IP network gets its own IP address, which is how the other networked devices find it. For instance, you can open www.whatismyip.com on your home PC, and it will show its IP address, assigned by your internet provider. DICOM Application Entity (commonly abbreviated as AE, see Sect. 5.3.8) generally corresponds to any DICOM application on a networked DICOM device; for example, a DICOM server (archive), imaging workstation, film printer, or image acquisition device (modality). These four examples cover some 95% of all AEs that you will likely find in a clinical environment, so I will use them as our standard examples. In addition to standard IP address, DICOM assigns to each AE its own DICOM name known as its Application Entity Title (AET). In DICOM, AET is encoded with the AE VR. As you might remember from the VR table in Sect. 5.3, the AE can have up to 16 characters. A practical approach for AET naming is to use either the application name (for example, PACSSERVER), or the computer name/location (DRBOBOFFICE); preferably without punctuation signs or spaces, and in uppercase to avoid ambiguity. This makes it simple to maintain and easy to identify.

7.1

Identifying Units on the DICOM Network

119

Unfortunately, certain PACS companies are infamous for using counterintuitive AE titles. When PACS software is installed at your site, make sure the AETs are assigned in a clear and consistent manner.

AEs as DICOM Applications

Important: As the name suggests, AE titles are used to label applications, not computers. Although in many cases you will have only one DICOM application running on each device, nothing prevents you from installing several of them on a single computer – server, workstation, DICOM printer server, and so on. In fact, this is a very common real-life situation. In this case, each DICOM application gets a different AE title and the other DICOM devices can talk to a particular application rather than to the whole computer.

All DICOM networking takes place between AEs when they exchange messages and data in DICOM format. If you are familiar with networking, you might know that TCP/IP always sends data to particular ports on each computer.1 Ports are numbered from 0 to 65535. For example, your web browser (HTTP2 protocol, based on TCP/IP) uses port 80 and your email (SMTP3 protocol, also TCP/IP based) uses port 25. DICOM’s default port is 104, but when you install your DICOM software, you can tune it to any other available port number, as long as: 1. You keep it consistent for all networked units; that is, as long as the receiving units use the same port as the sending units. 2. The port number is not already taken by another application. For example, using port 80 for DICOM would be a bit radical because it is traditionally used by web browsers, and won’t be available for anything else. If you do not like the standard DICOM port 104, try using ports with high numbers (say, 10000 and up); their chances of being used are usually lower. Port number comes in very handy when you have several DICOM applications on a single computer. While their different AE titles identify them as different DICOM applications, different port numbers separate them on the network. Even if they share the same IP address, each application gets called on its own port. Moreover, a single AE can have two ports: one to send data and another to receive data. Most current DICOM software provides this dual-port support for their AEs.

1

If this is new to you, think about computer names as street names, ports as house numbers, and AEs as residents’ names. You need to know all that to send a letter, right? 2 HyperText Transfer Protocol. 3 Simple Mail Transfer Protocol.

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Connecting the Unconnectable

If you are shopping for DICOM software, make sure that it gives you complete freedom to configure AE parameters, both local and remote. Local corresponds with the application and remote corresponds with other AEs communicating with the software. Open the AE setup window in your DICOM software. You should not see any preset, read-only AE titles or impossible-to-modify port numbers. For example, in one recent project that required connecting a server from company X to a server from company Y, we discovered that: 1. X wanted to use two different ports – send port and receive port – where the send port had to be 104 and the receive port had to be anything but 104. 2. Y wanted to use a single port for send and receive. As a result, when X was asking for two different ports, Y was insisting on one; essentially making any connection between the two impossible. There is no rational explanation for this poor design on the parts of both X and Y. Likewise, there is no practical reason for insisting on using particular port numbers.

That’s pretty much all you need to know about DICOM networking settings. Therefore, to configure your device on a DICOM network, you must assign to it a consistent: 1. AET (AE title) – Preferably alphanumeric, up to 16 characters. Think about easyto-understand “CTWORKSTATION1”, “ARCHIVE”, or “PACS_SERVER”. 2. AE IP address – Make sure it is reserved for this AE and will not change. 3. AE port number – Pick any (if not 104) and keep it consistent among all connected AEs. If you do not run multiple applications on the same computer, use a single port number throughout your entire DICOM network (Fig. 7.2). AE properties need to be set for any DICOM application included in your network; all DICOM applications will have some menu or configuration utility to do so. Look for menu items such as “DICOM Properties,” or “Add DICOM server (node),” and the like. AE setup is often performed by the device support personnel; and being a 5-min job, it ironically often takes several hours, if not days, to complete. Why? Because the support cannot be located or scheduled; because the configuration utility password was lost; because the entire PACS has to be rebooted; and for many, many other reasons you cannot imagine. Because of all these “becauses,” device manufacturers like to charge for this service, often asking for a few thousand dollars for something a child can do. Therefore, plan any AE configuration ahead of time, and make sure you have all the pieces in place – this will really save you time and money.

7.2

Services and Data

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Who are You?

Although it is not required by the DICOM standard, many DICOM manufacturers implement additional AE verification logic, requiring that connected AEs have complete knowledge of each other’s configuration. In the DICOM standard, if AE X wants to connect to AE Y, X needs to know the AE configuration for Y. This makes sense because X has to know where to find Y on the network (name, IP, port). What is often required by some devices in addition to this is Y knowing what X is, even if Y never initiates any connections to X. This mutual awareness could be presented by some as a coarse security feature (don’t talk to strangers), but in reality it is a rather annoying hassle: you added archive settings to your CT scanner, the scanner attempts to send images to the archive, and nothing works – you have to spend another day chasing your archive support. To avoid it, always add AE configuration symmetrically: if you add X’s configuration to device Y, always add Y’s configuration to device X. In our example, don’t forget to add CT scanner settings to the archive.

Fig. 7.2 Configuring AE specs in various DICOM software (AE title, IP address and port)

7.2

Services and Data

The model of data processing and exchange adopted by DICOM is classy and elegant – DICOM Application Entities provide services to each other. One DICOM entity can request a service from another, and that entity provides service to the first.

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Fig. 7.3 SCU-SCP model SCU

Service Class SCP

In DICOM lingo, a service-requesting AE is viewed as a Service Class User (SCU), and a service-providing AE is viewed as a Service Class Provider (SCP). In other words, AEs can play SCP or SCU application roles to communicate with each other (Fig. 7.3). As the SCP and SCU names suggest, all DICOM services are rendered on the level of DICOM Service Classes. Service Classes in DICOM bind DICOM data with data-processing functions. In more strict terms, DICOM Service Class associates one or more DICOM Information Objects (IODs) with one or more commands. Consider printing digital images on film, for example. The DICOM Print Management Service Class is responsible for printing (command) various images (IODs, such as CT or MR images). Consequently, any DICOM printer can provide this service (that is, act as a Print Management SCP). Any DICOM device sending images to the printer requests the service – acting as a Print Management SCU. In this section, you will become more acquainted with the structure and meaning of DICOM services. I need to say that while the DICOM standard is doing a particularly great job detailing the contents of its services, the overall relationships between them can often look extremely confusing because they are spread over multiple DICOM volumes and sometimes seasoned with inconsistent terminology. Reading this section should help.

7.2.1

DIMSE Services

I hope that by now you are becoming comfortable with the structure of DICOM IODs. So let’s take a closer look at another part of any service class definition: IODprocessing commands. How do DICOM applications (AEs) ask each other for services? Essentially the same way we humans do. DICOM AEs send service messages to each other, requesting or providing service information. This is why all service commands are known in DICOM as DICOM Message Service Elements (DIMSE). DIMSE protocol sets the rules for DICOM service exchange – the backbone of DICOM networking. Consequently,

7.2

Services and Data

123

(data elements)

DICOM Service in Real World

DICOM data dictionary

DICOM abstraction and standardization

Group 0000

Groups 0008 and higher

DIMSE service with optional data object Type

Store

Message ID

24

Priority

Medium

SOP UID

1.2.840….

Data Set Type

Yes (CT image)

Patient

John Smith

ID

123456

Modality

CT

Image Width Sex

512 M

DICOM Command Object

DICOM Data Object (optional, if Data Set is attached to command object)

Fig. 7.4 Building DICOM DIMSE

each DIMSE service usually has request and response message components. Requests are sent by the SCU AEs (for example, by a CT scanner trying to store a new CT image on an archive) and responses are provided by SCP AEs (such as a CT archive). We can continue with our human analogy a bit further. To exchange messages over a network we need to write them down and put them into DIMSE network envelopes. The writing part is already known to us; DICOM writes its network objects with the same VR rules that we learned in Chap. 5. But DICOM needs to distinguish service attributes from data attributes, so DICOM reserves a single 0000 group for all service tags (which are few) and calls service objects DICOM Command Objects – as opposed to DICOM Data Objectss, which we reviewed earlier. What if we need to pass data with the service; for example, we need to send an image to an archive? Look at Fig. 7.4. There is a Data Set Type (0000, 0800) attribute at the end of each service object. If this attribute is set to 0101 (DICOM’s NULL), the service does not need to transfer DICOM data – everything is contained in the service attributes. However, if the Data Set Type flag is set to anything besides 0101, the service Command Object will be followed by the Data Object, sent immediately afterward because data objects cannot travel by themselves. So DICOM services act as shuttles, shipping data between Application Entities. DIMSE services dealing with composite data are called “DIMSE-C” services; and DIMSE services dealing with normalized data are called “DIMSE-N” services (see Sect. 5.7.5). For that reason, a “C” or “N” is often prefixed to service names; for example, the C-Store service stores DICOM images (composite objects).

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Requesting AE C-Echo-Rq Request to verify DICOM connectivity

C-Echo-Rsp Response from DICOM-connected unit

Responding AE

Fig. 7.5 C-Echo protocol

DIMSE messages with service requests are labeled with an Rq suffix; for example, C-Store-Rq (composite store request) is what a CT scanner sends to a digital archive, requesting image storage. DIMSE messages with service responses are labeled with an Rsp suffix; for example, C-Store-Rsp is used by an archive to reply to the scanner. That should be enough of an abstract introduction, don’t you think? Quite often, examples work better than definitions, so let’s consider the most basic DIMSE services just to have a better idea of how they work.

7.2.2

Simple DIMSE Example: C-Echo

By far, C-Echo is the simplest DIMSE service. It is used to verify that one DICOM AE is connected to another.4 Accordingly, this simplicity makes C-Echo the most fundamental and frequently used service and here is why: • It is not enough to know that two DICOM devices are physically connected by a network cable. • It is not enough to be able to ping5 one device from another. Even though pinging proves that the devices are TCP/IP-networked, it provides no indication that they are DICOM-networked (or run DICOM software at all). We did not talk about AE titles, ports, and IPs in vain. The only way to verify that two AEs are configured properly is to C-Echo one of them from the other. The execution model of a C-Echo DIMSE is really straightforward, as illustrated in Fig. 7.5. The requesting AE sends a C-Echo request (C-Echo-Rq). If its peer AE replies with a valid response message (C-Echo-Rsp), the two are indeed properly DICOM-connected. The contents of the C-Echo-Rq and C-Echo-Rsp messages set by DIMSE protocol are shown in Tables 7.1 and 7.2; they leave little room for implementation errors.

4 5

For this reason, C-Echo has been nicknamed “DICOM ping” – we will talk about it in a bit. For readers less familiar with IT aspects of this matter: “ping” is a simple command, that one computer can send to another to verify that they are connected on the network.

7.2

Services and Data

Table 7.1 C-Echo-Rq Message field Command Group Lengtha

125

Tag (0000,0000)

VR UL

Affected Service Class UID Command Field Message ID

(0000,0002) (0000,0100) (0000,0110)

UI US US

Data Set Type

(0000,0800)

US

Value/description The even number of bytes from the end of the (0000,0000) value field to the end of the C-Echo-Rq message 1.2.840.10008.1.1 0030 Unique numerical ID for this message 0101

a

This item encodes the total length of all following items from 0000 group, as we learned in Sect. 5.5.2

Table 7.2 C-Echo-Rsp Message field Command Group Length

Tag (0000,0000)

VR UL

Affected Service Class UID Command Field Message ID Being Responded To

(0000,0002) (0000,0100) (0000,0120)

UI US US

Data Set Type Status

(0000,0800) (0000,0900)

US US

Value/description The even number of bytes from the end of the value field to the beginning of the next group 1.2.840.10008.1.1 8030 Shall be set to the value of the Message ID (0000,0110) field used in associated C-Echo-Rq Message 0101 0000

Let’s look at a few items in these tables: • Second column (Group, Element) tags, as we already know, encode different data fields in the DICOM Data Dictionary. Because we deal with services (that is, DICOM command objects) we use only command elements from the group number 0000. Their VRs (UL, UI, US) define their data formats, as we discussed earlier. • “Affected Service Class UID” contains the unique identifier corresponding to the C-Echo service: 1.2.840.10008.1.1. All DIMSE services have their unique ID strings (makes sense, this is how we can recognize them in DIMSE messages). The need for this identifier will become more evident as we discuss ServiceObject Pairs later. • Command Field, similarly, contains a predefined ID for this message type: it is always 0030 for C-Echo-Rq and 8030 for the C-Echo-Rsp (because these numbers have binary US VR, they are hexadecimal). When AE receives a DIMSE command, it will look first at the command field tag: if it is 0030, the command is identified as C-Echo-Rq. Leading digit 8 in the Rsp Command Field is commonly used in other DIMSE services to differentiate between Rsp and Rq messages.

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• Data Set Type parameter is set to 0101, which in DICOM means NULL data (no data attached). C-Echo is purely a command object that does not convey any data (such as images, patient information, and so on – anything encoded with group numbers other than 0000, see Fig. 7.4). • Message ID in C-Echo-Rq contains a uniquely generated number corresponding to this message. All DIMSE request messages have unique IDs. Unlike DICOM UIDs, message IDs are short 2-byte numbers (US VR) simply meant to identify each message during its brief life span. When an Rsp message is constructed, the Message ID from the Rq message is copied into the (0000,0120) field – this is how DICOM pairs up requesting and responding messages. • Imagine a busy DICOM network on which the same AE can receive dozens of DIMSE messages each second. Message ID in this case becomes the only way to distinguish among them, keep track of the requesting AEs, and reply to them correctly. • Status field – always set to 0000 in the C-Echo-Rsp message – indicates success. This means that the peer AE has received and understood the C-Echo-Rq, so it replies with a success C-Echo-Rsp. There are no other options for C-Echo status; simply receiving a C-Echo-Rsp reply implies that C-Echo was successful. The C-Echo message is considered failed only if no response returns during the preset timeout interval (usually several seconds). Nonzero values in the Status field are used by the other DIMSE services to return various warning or error messages, in case something goes wrong (Table 7.3). For example, if a DIMSE service tries to print an image and the image is not available, an error value is reported in the Status field for that service. With all this knowledge, take a look at the following Table 7.4 sample C-Echo-Rq in its final DICOM encoding. Just like any DICOM command object, it follows implicit VR data encoding (default DICOM encoding, see table in Sect. 5.5.1). The sequence of 68 bytes in the Binary row is exactly what will be sent on a DICOM network from one AE to another as the C-Echo-Rq. Note that this sequence is in Little Endian order (see Sect. 5.2); when in multi-byte numbers, the least significant bytes go first. For example, bytes 15 and 16 form element number e = 0002 = 02 (least significant byte) and 00 (most significant byte). The C-Echo-Rsp is built in a similar manner and returned as shown in Table 7.5.

Table 7.3 Status types and their value ranges (X stands for any hexadecimal digit) Status type Value Success 0000 Warning 0001 or Bxxx Failure Axxx or Cxxx Cancel FE00 Pending FF00 and FF01 Warning and failure 01xx and 02xx

Table 7.4 C-Echo-Rq in final DICOM byte encoding Byte # 1 2 3 4 5 6 7 8 9 10 11 12 13 14 Decimal 0 0 0 0 4 0 0 0 56 0 0 0 0 0 Binary 00 00 00 00 04 00 00 00 38 00 00 00 00 00 g = 0000 e = 0000 VR length = 4 VR value = 56 g = 0000 Byte # 17 18 19 20 21 22 23 24 25 26 27 28 29 30 Decimal 18 0 0 0 ‘1’ ‘.’ ‘2’ ‘.’ ‘8’ ‘4’ ‘0’ ‘.’ ‘1’ ‘0’ Binary 12 00 00 00 31 2e 32 2e 38 34 30 2e 31 30 VR length = 18 VR value = 1.2.840.10008.1.1 (17 characters and trailing padding blank, 0 byte) Byte # 33 34 35 36 37 38 39 40 41 42 43 44 45 46 Decimal ‘8’ ‘.’ ‘1’ ‘.’ ‘1’ 0 0 0 0 1 2 0 0 0 Binary 38 2e 31 2e 31 00 00 00 00 01 02 00 00 00 g = 0000 e = 0100 VR length = 2 Byte # 49 50 51 52 53 54 55 56 57 58 59 60 61 62 Decimal 0 0 16 1 2 0 0 0 2 0 0 0 0 8 Binary 00 00 10 01 02 00 00 00 02 00 00 00 00 08 g = 0000 e = 0110 VR length = 2 Val = 0x0020 g = 0000 e = 0800 Byte # 65 66 67 68 Total DICOM object length: 68 bytes = 12 bytes + 56 bytes, Decimal 0 0 1 1 where: Binary 00 00 01 01 (0000,0000) “group length” element length: 12 bytes =0002 Val = 0x0101 Length after (0000,0000) element: 56 bytes (equals to (0000,0000) element value)

32 ‘0’ 30

16 0 00

47 48 48’0’ 0 30’0’ 00 Val = 0x0030 63 64 2 0 02 00 VR length=

15 2 02 e = 0002 31 ‘0’ 30

7.2 Services and Data 127

Byte # Decimal Binary

Byte # Decimal Binary

Byte # Decimal Binary

Byte # Decimal Binary

Table 7.5 Byte # Decimal Binary

C-Echo-Rsp in final DICOM byte encoding 1 2 3 4 5 6 7 8 9 10 11 12 13 0 0 0 0 4 0 0 0 56 0 0 0 0 00 00 00 00 04 00 00 00 38 00 00 00 00 g = 0000 e = 0000 VR length = 4 VR value = 56 g = 0000 17 18 19 20 21 22 23 24 25 26 27 28 29 18 0 0 0 ‘1’ ‘.’ ‘2’ ‘.’ ‘8’ ‘4’ ‘0’ ‘.’ ‘1’ 12 00 00 00 31 2e 32 2e 38 34 30 2e 31 VR length = 18 VR value = 1.2.840.10008.1.1 (17 characters and trailing padding blank, 0 byte) 33 34 35 36 37 38 39 40 41 42 43 44 45 ‘8’ ‘.’ ‘1’ ‘.’ ‘1’ 0 0 0 0 1 2 0 0 38 2e 31 2e 31 00 00 00 00 01 02 00 00 g = 0000 e = 0100 VR length = 2 49 50 51 52 53 54 55 56 57 58 59 60 61 0 0 1 2 0 0 0 2 0 0 0 0 32 00 00 01 02 00 00 00 02 00 00 00 00 20 g = 0000 VR length = 2 Val = 0020 g = 0000 e = 0800 e = 0120 65 66 67 68 Total DICOM object length: 68 bytes = 12 bytes + 56 bytes, 0 0 1 1 where: 00 00 01 01 (0000,0000) “group length” element length: 12 bytes =0002 Val = 0101 Length after (0000,0000) element: 56 bytes (equals to (0000,0000) element value) 62 8 08

46 0 00

30 ‘0’ 30

14 0 00

32 ‘0’ 30

16 0 00

47 48 48’0’ 128 30’0’ 80 Val = 8030 63 64 2 0 02 00 VR length=

15 2 02 e = 0002 31 ‘0’ 30

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We marked the fields that changed in the C-Echo-Rsp compared to the C-Echo-Rq. First is the command field (0000,0100), which reflects the change in the command type. Second is the message id, which in the C-Echo-Rsp is stored in (0000,0120) and corresponds to the value in (0000,0110) in the C-Echo-Rq. As you can see, there is really nothing baffling in the C-Echo DIMSE service; nearly all service parameters are fixed. If you are writing DICOM software, the only parameter you should generate in the C-Echo protocol is the Message ID number. It has to be different for each new C-Echo request, and matched in the C-Echo-Rsp (0000,0120) field. As long as this is done, you are good to go. Sancta Simplicitas

When I got my first app-loaded smartphone, I went into its online store searching for DICOM products. Guess what was the very first I found: a DICOM C-Echo app of course! As you can see from above, one can write C-Echo code with very little DICOM background. Why would one need to have C-Echo on a smartphone? Apparently, texting your friends is not cool anymore. Consider DICOMing them instead; and do not forget to attach your chest X-rays! ☺

7.2.3

SOP: Service-Object Pairs

Let’s take look at what we’ve got so far in our DICOM toolbox: • First, we have information objects (IODs) to define DICOM data. • Next we have DIMSE services to define DICOM commands. Naturally enough, commands need to be applied to some data (printing CT images, storing MR series, and so on). So we pair up corresponding DIMSE services and IOD objects and call them Service-Object Pairs (SOPs); that is, we match a DICOM data object (IOD) with the appropriate instructions on how the data needs to be processed (service). All the time that I was talking about services and data, I was essentially talking about SOPs. Figure 7.6 shows the general structure of a DICOM SOP Class. As you can see, it indeed merges functionality (services) with data (IODs). Note that because a group of services might be needed to process certain IODs, SOPs merge IODs with DIMSE service groups. DICOM PS3.6 provides a list of all standard DICOM SOPs and it is not that long (see Appendix A.3). In this list, each SOP has a descriptive name and an associated DICOM UID. When DICOM needs to define what to process and how, it always does so in terms of SOPs. This makes SOP Classes particularly important – they really tell you what to expect from any DICOM application. All DICOM Conformance Statements are written in the SOP language. I briefly touched on this subject before when I was talking about DICOM Objects and IODs, but the time was not quite right for looking at the network functionality. Now, the time is right.

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SOP Class Example: CT image storage

DIMSE service group Example: Store

Service 1

Service 2

IOD Example: CT image

applies to

Service …

Attribute 1

Attribute 2

Attribute …

Fig. 7.6 SOP class structure Table 7.6 Verification SOP SOP class name Verification

SOP class UID 1.2.840.10008.1.1

In the following subsections, we will review the most frequently used DICOM SOP Classes starting with the most basic and commonly supported.

7.2.4

Verification SOP

The Verification SOP verifies DICOM connectivity between two Application Entities. Haven’t we seen this 1.2.840.10008.1.1 from Table 7.6 somewhere before? That’s right; this string was used as an SOP parameter in C-Echo. The Verification SOP has C-Echo as its DIMSE part, and because connectivity verification does not require data processing, it has no IOD (Fig. 7.7). As a result, the Verification SOP does the exact same thing as C-Echo: it verifies DICOM connectivity between two DICOM applications (AEs). The Verification SCU sends a Verification request (C-Echo-Rq) to another application. If that other device is properly connected and supports Verification SCP, it replies with a C-EchoRsp to confirm that it is connected. Essentially, the Verification SCU asks its peer: “Can you talk DICOM to me?” and hopes to hear “Yes.” What happens if the Verification SOP fails, that is, if no response returns before the connection times out? In most cases, this means one of two possible situations: 1. You have a general, network connectivity error such as unplugged network cable, incorrect IP address, blocked communication (firewall), network downtime, or similar problem. To troubleshoot, make sure that your AEs are properly networked and you can ping one AE from the other.

7.2

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Verification SOP

DIMSE: C-Echo

IOD Identifier: none

Fig. 7.7 Verification SOP

2. The network is okay, but the DICOM configuration is wrong. This usually means that one of the two DICOM communicating devices does not know the correct IP address, port, or DICOM title of the other device. If any of these parameters is wrong, the sending C-Echo-Rq AE is simply knocking on the wrong door. To troubleshoot, please make sure that each AE knows the configuration (IP, port, title) of its peer. The Verification SOP Class plays the most fundamental role in controlling the sanity of any DICOM network. For that reason, it is commonly implemented in most DICOM interfaces as some kind of Verify Connectivity button that users can click to make sure the remote device is still available to DICOM. Should anything go wrong in your DICOM connectivity, start troubleshooting with the Verification SOP. Consequently, avoid buying DICOM devices and software that do not implement the Verification SOP SCP. As this definition implies, they won’t be able to reply to the DICOM verification requests – in plain words, you will have no means of verifying DICOM connectivity to them.

Real Case: Verification SCP and Security?

On one occasion, I was connecting to a CR unit from a well-known imaging vendor, and discovered that the unit failed to support the Verification SOP SCP. “No wonder,” commented the vendor’s engineer involved in the installation, “we do not want you to export the image data from the CR. This is a security feature.” For all people who make similar statements to mask their ignorance: as we just learned, the Verification SOP does not transmit image data or any other confidential information. Consequently, it cannot be used for stealing any information, proprietary or not. Never build security on obscurity, and never use security as an excuse to cover up poor functional design.

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7.3

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DICOM SOPs: Basic

Storage

If C-Echo (Verification SOP) is important because of its role in DICOM connectivity checks, C-Store (Storage SOP) is the DICOM data workhorse responsible for moving DICOM images (and other data types) between the AEs. Despite a somewhat misleading name, C-Store sends DICOM data objects from one AE to another over a DICOM network. Because different objects imply different processing, DICOM assigns a separate SOP class to each modality or data type (for example CT Image Storage SOP, MR Image Storage SOP, and so on). For that reason, DICOM Storage is represented by a family of data-specific storage SOPs, each with its own unique identifier (UID). You can find all Storage SOPs in the Appendix, Sect. A.3; for brevity, I show the most frequently used SOPs in Table 7.7. Apart from the modality-specific Storage SOP type (UID), everything else in DICOM storage works the same for all storage modalities. First of all, the structure of all storage SOP classes is the same (Fig. 7.8). Here, the DIMSE C-Store service, as always, contains the command part and the IOD attached to it contains the actual object to be stored. The C-Store IOD is the modality-specific part and the C-Store DIMSE is very much modalityindependent. As you can see from the C-Store SOP table, DICOM Storage can be used for different data types: images, waveforms, reports, and more. Images are the most common and frequently used, so I refer mostly to them for the rest of this chapter.

7.3.1

C-Store IOD

There is no mystery surrounding the C-Store IOD component – it always contains the data to be stored. The type of this data (MR, CT, CR, US, and more) defines the particular C-Store SOP class UID as outlined in Table 7.7. If you need to store multiple images (usually the case) each image is transmitted with its own separate C-Store request. That is, one cannot batch multiple IOD instances in a single C-Store. The only exception would be multiframe IODs (such as Ultrasound Multiframe Image Storage, 1.2.840.10008.5.1.4.1.1.3.1) where a single IOD instance (image) contains multiple image frames. This might sound confusing, but the implication is simple: video clips (such as ultrasound cine loops) are viewed by DICOM as single images with multiple frames and can therefore be sent with a single C-Store request. In contrast, CT or MR series would be always processed with a new C-Store for each image. On one hand, this introduces certain processing and communication overhead,6 but on the other hand it ensures that the outcome of each image transmission is well known – pending, failed, or sent successfully.

6

Proprietary, non-DICOM-compliant formats, used internally by medical device manufacturers, would often prefer to batch any image series into a single data buffer and send it along in one shot.

7.3

Storage

133

C-Store Bottlenecks

If you implement DICOM software that uses C-Store to send image series, never interpret a single image store failure as failure for the entire series. A single image in a series can fail C-Store for many totally benign reasons (for example, the image already exists at the destination) and it should not stop the transmission of the remaining images. More importantly, never interpret a C-Store failure for some images as an excuse for halting any subsequent communications. I had this experience with one CR device that was sending images to a digital archive. It got stuck on one particular study with an incorrectly entered patient ID (which was illegally blank) – the destination archive recognized the ID problem and refused to accept the study. Surprisingly enough, the CR unit interpreted this single-study failure as a major disaster and stopped sending all subsequent studies after the one that failed; even though the new ones had absolutely correct patient IDs! In a short time, all unsent studies queued on the CR filled up its local hard drive and the unit refused to process any new patients. This provides an excellent example of how a single minor problem, magnified by very poor software design, bottlenecked the entire PACS workflow.

7.3.2

C-Store DIMSE

C-Store takes care of instructing the image-accepting AE on what needs to be done. Just like C-Echo, C-Store has a request part (sent from the C-Store SCU) and a response part (replied from the C-Store SCP). If we compare Table 7.8 to the C-Echo-Rq studied earlier (see Sect. 7.2.2), we find a few new parameters, which are described below: • Priority – As the table suggests, priority can be low, medium (normal), or high. Many other DIMSE messages include a priority field in their specifications, but its support is optional and defaults to medium (0000). In reality, different message priorities are almost never implemented by DICOM manufacturers and this is noted in their Conformance Statements. This makes practical sense because maintaining a functional PACS network is much more important than prioritizing messages, and any priority-assigning mechanism only adds unnecessary complexity and unpredictability. Delays in data acquisition, transmission, and retrieval – inevitable in any real clinical environment – have much stronger impact on any messaging scheme than predefined message priorities. In short, SOP message priority is rarely needed. • Affected SOP instance UID – This is self-explanatory. The C-Store-Rq message is followed by an image to be stored, and this image has its own unique identifier, which is included in the field (see Sect. 5.6).

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Table 7.7 Storage SOP SOP class name SOP class UID Computed Radiography Image Storage 1.2.840.10008.5.1.4.1.1.1 Digital X-Ray Image Storage – For Presentation 1.2.840.10008.5.1.4.1.1.1.1 Digital X-Ray Image Storage – For Processing 1.2.840.10008.5.1.4.1.1.1.1.1 Digital Mammography X-Ray Image Storage – For 1.2.840.10008.5.1.4.1.1.1.2 Presentation X-Ray Angiographic Image Storage 1.2.840.10008.5.1.4.1.1.12.1 Positron Emission Tomography Image Storage 1.2.840.10008.5.1.4.1.1.128 CT Image Storage 1.2.840.10008.5.1.4.1.1.2 Enhanced CT Image Storage 1.2.840.10008.5.1.4.1.1.2.1 Nuclear Medicine Image Storage 1.2.840.10008.5.1.4.1.1.20 Ultrasound Multiframe Image Storage (Retired) 1.2.840.10008.5.1.4.1.1.3 Ultrasound Multiframe Image Storage 1.2.840.10008.5.1.4.1.1.3.1 MR Image Storage 1.2.840.10008.5.1.4.1.1.4 Enhanced MR Image Storage 1.2.840.10008.5.1.4.1.1.4.1 MR Spectroscopy Storage 1.2.840.10008.5.1.4.1.1.4.2 Basic Text SR 1.2.840.10008.5.1.4.1.1.88.11 Enhanced SR 1.2.840.10008.5.1.4.1.1.88.22 Comprehensive SR 1.2.840.10008.5.1.4.1.1.88.33 RT Image Storage 1.2.840.10008.5.1.4.1.1.481.1 Nuclear Medicine Image Storage (Retired) 1.2.840.10008.5.1.4.1.1.5 Ultrasound Image Storage (Retired) 1.2.840.10008.5.1.4.1.1.6 Ultrasound Image Storage 1.2.840.10008.5.1.4.1.1.6.1 Secondary Capture Image Storage 1.2.840.10008.5.1.4.1.1.7 12-lead ECG Waveform Storage 1.2.840.10008.5.1.4.1.1.9.1.1 General ECG Waveform Storage 1.2.840.10008.5.1.4.1.1.9.1.2 Basic Voice Audio Waveform Storage 1.2.840.10008.5.1.4.1.1.9.4.1 1.2.840.10008.5.1.4.38.1 Hanging Protocol Storagea a Hanging protocols define image layout on the monitor screen. The term comes from the old times when radiologists used to hang films on light boxes

Storage SOP (for specific data type)

DIMSE: C-Store

Fig. 7.8 Storage SOP

IOD Identifier: data to be stored.

7.3

Storage

Table 7.8 C-Store-Rq Message field Command Group Lengtha

135

Tag (0000,0000)

Affected SOP Class UID

(0000,0002)

Command Field Message ID

(0000,0100) (0000,0110)

Priority

(0000,0700)

Data Set Type Affected SOP Instance UID

(0000,0800) (0000,1000)

Move Originator Application Entity Title

(0000,1030)

Move Originator Message ID

(0000,1031)

a This item encodes the total length of Sect. 5.5.2

VR UL

Value/description The even number of bytes from the end of the (0000,0000) value field to the end of the C-Store-Rq message UI Contains SOP UID for this image type – for example, 1.2.840.10008.5.1.4.1.1.2 for CT Image Store US 0001 US Unique numerical ID for this message US One of the following choices: 0002 (for low priority) 0000 (for medium priority) 0001 (for high priority) US Anything different from 0101 UI Contains the UID of the SOP Instance to be stored (that is, UID of the image to be stored). AE Contains the DICOM AE Title of the DICOM AE which invoked the C-MOVE operation from which this C-STORE sub-operation is being performed US Contains the Message ID (0000,0110) of the C-MOVE-RQ Message from which this C-STORE sub-operations is being performed all following items from 0000 group, as we learned in

• Move Originator Title and Message ID fields – As we will soon see in the C-Move SOP section, C-Store can be invoked by other DIMSE commands, such as C-Get or C-Move. In this case, the title of the invoking AE and the original message ID will be included in these fields. • Data set type – You might remember that in C-Echo this field was set to 0101, which means NULL (no data attached) in DICOM. Consequently, a C-Store-Rq followed by data (the image to be stored) must have this field set to anything but 0101. What this is going to be is decided by each DICOM manufacturer and really does not matter. Some might use 0102 and some might use 0000 – ironically, in this context, 0000 indicates not-NULL (attached data) as opposed to the DICOM NULL, 0101.

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C-Store SCU (Example: Modality) Store SOP Request with image: Store newly acquired image

Store SOP Response: Image stored C-Store SCP (Example: Archive)

Fig. 7.9 DICOM C-Store

Table 7.9 C-Store-Rsp Message field Command Group Length

Tag (0000,0000)

VR UL

Affected SOP Class UID

(0000,0002)

UI

Command Field Message ID Being Responded To

(0000,0100) (0000,0120)

US US

Data Set Type

(0000,0800)

US

Affected SOP Instance UID

(0000,1000)

UI

Status

(0000,0900)

US

Value/description The even number of bytes from the end of the (0000,0000) value field to the end of the C-Store-Rsp message Contains SOP UID for this image type – for example, 1.2.840.10008.5.1.4.1.1.2 for CT Image Store 8001 Shall be set to the value of the Message ID (0000,0110) field used in associated C-Store-Rq Message 0101 (meaning no data attached to C-Store-Rsp) Contains the UID of the SOP Instance to be stored (that is, UID of the image to be stored) 0000 (if successful), FF00 (if pending), or other vendor-supported values for warnings and errors (see vendor’s Conformance statement)

Because the Data Set Type field is not NULL, a C-Store-Rq message will be immediately followed by the image to be stored (one and only one image object). In other words, a C-Store-Rq always sends an image to its peer AE, asking for storage (Fig. 7.9). The C-Store-Rsp returns from the data-receiving AE. It is shown in Table 7.9. Nearly everything in a C-Store-Rsp is derived from the original C-Store-Rq

7.4

Query: Find

Table 7.10 Query SOP SOP class name Patient Root Q/R Find Study Root Q/R Find Patient-Study Root Q/R Find (Retired)

137

SOP class UID 1.2.840.10008.5.1.4.1.2.1.1 1.2.840.10008.5.1.4.1.2.2.1 1.2.840.10008.5.1.4.1.2.3.1

message, except the ever-important Status field. Status tells you whether the image was stored (sent) successfully (Status = 0000), is still in the process of transmission (Status = FF00), or caused any warnings/errors (other values of Status). The list of data-dependent C-Store SOPs keeps changing in every new DICOM revision; old data/image types are eventually retired while new types are added. If you are buying a new DICOM software or unit, always make sure that your data type is listed in the unit’s DICOM Conformance Statement.

7.4

Query: Find

Now we can verify DICOM connectivity. We can even send images from one AE to another. But how can we find out what data needs to be sent? One of the principal advantages of DICOM networking over simple file-sharing protocols is the ability to search for specific DICOM data, using DICOM C-Find messages. Three C-Find SOPs classes are provided to implement DICOM queries as shown in Table 7.10. Searching for imaging data is not modality-specific, so C-Find does not use a multitude of C-Store-like, modality-based SOPs. In this respect, C-Find is very similar to C-Echo (Fig. 7.10). However, one thing about C-Find is still new to us; DICOM divides all C-Find data searches into three data levels: Patient, Study, and Patient-Study. Those levels are called roots and C-Find has a separate SOP to implement data searches on each of them. The need for DICOM query roots follows from the DICOM Patient-StudySeries-Image data hierarchy discussed in Sect. 5.6. DICOM organizes data into four hierarchical levels, and searching this data works best when limited to the same levels. For example, if the Patient Query/Retrieve root is supported, all searches will follow the Patient-Study-Series-Image hierarchy, starting from the Patient; with the Study Query/Retrieve root, they will be limited to Study-Series-Image levels only. In the latter case, all Patient attributes (such as patient name or ID) will be included in Study attributes to identify a study. Because the current radiology’s workflow is study-centric, the Study root is the most widely supported in DICOM applications and is usually always provided by

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C-Find SCU (Example: Workstation) Find SOP Request: Find today’s studies

Find SOP Response: List of today’s studies

C-Find SCP (Example: Archive)

Fig. 7.10 DICOM C-Find example: retrieving a list of all today’s studies from an archive

Find SOP

DIMSE: C-Find

IOD Identifier: search criteria

Fig. 7.11 Query SOP

default. Patient and Patient-Study SOPs for Query are rarely implemented. In fact, the Patient-Study root has been recently retired in the DICOM standard, meaning that it will not be supported in future editions of DICOM and it needs to be gradually excluded from DICOM implementations. Needless to say, the C-Find SOP follows the same DIMSE + IOD structure, as shown in Fig. 7.11. The DIMSE object (encoded as a DICOM command object, group 0000) conveys the C-Find message parameters, and the attached IOD object (encoded as a DICOM data object) contains the search criteria to be matched on the C-Find SCP.

7.4.1

A Few Words on Data Matching in DICOM

When a C-Find SCU sends a find request, it includes a set of attributes to be matched. These attributes work as search fields that need to be compared against the data on

7.4

Query: Find

139

the target AE. Consequently, DICOM offers a few ways of specifying the search values: • Wildcard matching – This one is already known to us. We first glanced at it in Sect. 5.3.2 when we talked about DICOM wildcards ( * ? ). Wildcard matches are commonly used to match strings, and not in DICOM only. For example, if we know that the patient name starts with “Smit”, we can set the Patient Name (0010,0010) attribute in the C-Find request to “Smit*”using the asterisk ( * ) to indicate any sequence of symbols. Smith, Smithson, and even Smit III the Great will match this wildcard search. Another DICOM wildcard, the question mark ( ? ), means a single unknown symbol, but it is rarely used for that reason; if we do not know the symbols, we more likely do not know how many there are. • List matching – This technique uses the backslash ( \ ) wildcard, which means “or.” For example, “Smith\Graham” specified in the patient name will be used to find all Smith and Graham patients. You can definitely combine list and wildcard matches, with something like “Smit*\Grah*” especially when you deal with potentially mistyped data (and manually entered patient names do get mistyped more than we might like). • Universal matching – This is the most general matching type. If the attribute to be matched has zero length, it will match everything. For example, if we provide a blank patient name in our C-Find query, all patient names in the target archive will be matched. Essentially, universal matching means anything and is identical to using the asterisk ( * ). However, there is a difference between providing a zerolength matching attribute in a C-Find request and not providing it at all. Including a zero-length attribute means that we want (or have to) use it for matching and we want to receive its matched values in a C-Find response. Not using an attribute means that we do not want it to be matched (it might not even be supported) and we don’t care about retrieving its values. For example, if we do not want to retrieve patient names, we need not include a Patient Name attribute, empty or not, in the C-Find request. • Range matching – This matching type is used for attributes that have ranges, such as date or time. In range matching, you can specify start and end attribute values, separated by a hyphen (-). Anything within this range will be matched. For example, range date match 20000201–20100202 means any date between February 1, 2000 and February 2, 2010. If either start or end is unknown, it can be omitted; so 20000201- means “February 1, 2000 and after.” • Sequence matching – Perhaps the most complex form of DICOM attribute matching, sequence matching is typically employed when an entire sequence of attributes (formed with the SQ VR, see Sect. 5.3.10) is used to match data. For example, to match a modality-scheduled patient study, DICOM uses a sequence of attributes such as patient ID, study date, and accession number. Each attribute in this sequence can have either exact values, or can use one of the above matching types. Grouping attributes into an SQ sequence works as a logical “and” (meaning that all attributes must match), as opposed to using the backslash ( \ ) wildcard as a logical “or.”

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• Single value matching – This simply means using the exact attribute value as the matching parameter: using “Smith” for patient name, “20000201” for date, and so on. Naturally enough, single value matched attributes must not contain wildcards or ranges. Single value matching is commonly used for various IDs and UIDs, and in particular for hierarchical key attributes (see Sect. 5.6.3). All attributes in DICOM data matching can be either required or optional. Required attributes, as the name suggests, must be present in the matching request. If we have no idea what their values might be, we simply insert them with blank values (universal matching). This also implies that we are guaranteed to receive the matched values. Optional attributes may be included if we are interested in using them. The DICOM standard usually specifies required and optional attributes for each query type (such as C-Find on Patient, Study, Series, or Image level), but you always need to check the DICOM Conformance Statements for the devices you are querying – they commonly override more generic DICOM specifications. This is understandable in part because attributes used on an ultrasound scanner will definitely differ from those used on a CT teaching archive. However, this always presents a certain implementation headache when you have to deal with different query attribute profiles for different devices. Finally, nearly all matching in DICOM is case-sensitive. Case-insensitive matches are permitted for certain attributes such as names (PN VR), where they can also be space- and accent-insensitive. Personally, I doubt that case-sensitivity brings any advantages to DICOM whatsoever: first, case can be easily altered when typed; second, they almost never matter. Worse, some major PACS interfaces require users to enter certain data using a certain case: for example, typing patient names in uppercase only. This quickly turns into a major annoyance when patient Smith cannot be found unless you type him as SMITH. Practically, it often forces users to “Caps Lock” their PACS keyboards, which, as we can easily guess, inevitably creates an abundance of other typing problems, especially if other software is used on the same workstation. DICOM and DICOM applications should not be case-sensitive whenever possible. Appendix A.4 gives you an idea of varying query attribute support from different DICOM manufacturers.

7.4.2

C-Find IOD

After our little voyage into the land of DICOM attribute matching, we can look at C-Find queries with a better level of understanding. C-Find IODs contain the search parameters to be matched on the C-Find service provider (digital archive, for example). Table 7.11 shows an example taken from a real Conformance Statement.7 As we discussed in Sect. 5.6.3, all data in DICOM follows the Patient-StudySeries-Image hierarchy, and C-Find queries are no exception; they must belong to one of those four levels. C-Find query level is reflected in the (0008,0052) attribute

7

Because this C-Find IOD sample is taken from a particular DICOM application, yours could have a different list of optional items.

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Query: Find

141

Table 7.11 Query parameters (Group, Required/ Element) Name optional Example (0008,0052) Query R STUDY Level

Matching (see Sect. 5.3.2) Can be PATIENT, STUDY, SERIES, IMAGE. This element defines the level or hierarchical search Wildcard matching: * stands for any substring (empty included),? stands for any single symbol, and \ means “or” Typically, exact value is needed. Some systems support wildcard ID matches Typically, range matching between two dates in YYYYMMDD format Typically, range matching between two times in HHMMSS format. If study dates were given as a range, then the start time belongs to the start date, and the end time to the end date. In our example we are looping for all studies done between 01:55:00 on December 31, 2009, and 23:55:59 on February 1, 2011 Typically, exact match. Accession numbers are often imported from Radiology Information System Almost always exact match Almost always wildcard matching, where keywords are used to locate studies with specific descriptions As for any names, wildcard matches are preferred

(0010,0010)

Patient’s name

R

Smit*\ Grah*

(0010,0020)

Patient ID Study date Study time

R

12345

R

20091231– 20110201 015500– 235559

(0008,0050)

Accession number

R

Abc789

(0020,0010) (0008,1030)

Study ID Study description

R O

1.234.567 *knee\ elbow*

(0008,0090)

Referring physician name Reading physician name Modalities in study Patient’s birth date

O

*Sinitsyn*

O

*Bakhtin*

As for any names, wildcard matches are preferred

O

MR\CT

Exact match or list match

O

19560101– 19860101

Range match. Used to determine patient age

(0008,0020) (0008,0030)

(0008,1060)

(0008,0061) (0010,0030)

R

(required), and can be Patient, Study, Series, or Image. This also means that, in addition to SOP roots, the following should apply at any C-Find search level: 1. Key level attribute values8 from all higher query levels must already be known. For example, before starting a Study-level search, we must know the Patient ID.

8

The key level attribute values in Patient-Study-Series-Image hierarchy, as we learned in Sect. 5.6.3, are Patient ID (0010,0020), Study Instance UID (0020,000D), Series Instance UID (0020,000E), and Image, or SOP Instance UID (0008,0018), respectively.

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2. Key level attribute values from the current level will be returned by the C-Find SCP. For example, in a Study-level search, “Study Instance UID” (0020,000E) will always be returned in response to any C-Find request. This enables us to proceed to the lower levels (Series, Image) according to rule 1 above. 3. In any C-Find request, we can search and match attributes only from the current C-Find level. For example, a Study-level search can search only the study attributes and cannot search individual series or images. Look at the sample IOD in Table 7.11; what level is it on? The answer follows immediately from the (0008,0052) value: it is the Study level. But even if you did not know this value, you could have reasoned differently. The IOD seems to contain Patient ID (Patient level), but does not yet have a Study Instance UID (Study level). So according to rules 1 and 2 above, it is clearly on the Study query level. This logic is also useful for pinpointing DICOM problems. For example, if the level we determine from the key attributes does not match the one declared in (0008,0052) the C-Find query is destined to fail. Certainly, you do not have to build your own level attribute tables. The DICOM Conformance Statement for each particular DICOM application lists all supported attributes for each supported level (and identifies them as required or optional). In fact, a particular device might not even support all four hierarchical levels – in which case, its Conformance Statement will contain only the levels supported.

Poor, Poor Modality

The most commonly level-misplaced attribute is imaging Modality (0008,0060). Knowing modality is important without question. However, most radiologists associate modality with a study and will likely be very disappointed if a study search (Study-level C-Find) does not return modality information. But this is a well-anticipated result – nearly all DICOM Conformance Statements list Modality as a Series-level attribute. This means two things: 1. A DICOM study can be multimodality. Few people are used to this concept, so here is a simple example: a CT study with a few 3D-reconstructed images added to it. Those 3D reconstruction screenshots are more likely stored with the SC (Screen Capture) modality value and not with CT. 2. To find study modality, you should either search at the Series level with the (0008,0060) Modality attribute, or use (0008,0061) the “Modalities in Study” attribute, which belongs to the Study level. Searching a series is cumbersome. It requires stepping one level down from the Study level, and it might not even be supported on the target device. On the other hand, the (0008,0061) Modalities in Study attribute is not always supported. This often makes retrieving a modality value problematic. But if we cannot fix our problems, we should at least understand them, right?

7.4

Query: Find

143

In some advanced applications, you can also use relational DICOM queries in which you need not follow any rigid hierarchical structure. If the relational queries are supported on the target device (C-Find SCP), you can throw pretty much anything you like into the C-Find IOD – certainly any attribute listed in the device’s DICOM Conformance Statement. This is called extended C-Find behavior as opposed to the hierarchical search baseline. It certainly makes C-Find implementation easier, but (and there always seems to be a but in any easy DICOM implementation) we should remember a couple things: 1. As I mentioned earlier, 99% of DICOM and medical imaging workflow is studycentric. Radiologists, physicians, and archives prefer to function on the welldefined Study level. If you give them the ability to mix and match whatever they like, they will not necessarily appreciate it. 2. Relational queries are rarely supported by DICOM applications. With all this in mind, relational queries can still be extremely useful for performing tough and unusual searches, but they do not seem to be in high demand in a typical just make it work clinical environment. Finally, there is one more important attribute not mentioned in Table 7.11: (0008,0056) Instance Availability. It takes one of the following values: • “ONLINE” means the IOD instances (such as images we are searching for) are immediately available for retrieval. • “NEARLINE” means the instances are still retrievable, but need to be loaded from relatively slow media such as optical disk or tape. • “OFFLINE” means that a manual intervention is needed before the instances may be retrieved. • “UNAVAILABLE” means the instances cannot be retrieved. Basically, (0008,0056) deals with PACS storage implementation, such as when the most recent cases are kept ONLINE (on fast spinning hard drives), but aging studies are moved to slower storage (NEARLINE tapes and disks) or simply shelved (OFFLINE). According to DICOM, (0008,0056) attribute “shall not be included in a C-FIND request” and can be provided in a C-Find response only (if the responding AE chooses to do so). For this reason, we skipped it in Table 7.11. In reality however, I’ve seen (0008,0056) being used in C-Find requests as well, which makes sense: one needs to ask about data availability before trying to pull it with C-Store. Otherwise, you may spend hours pushing the “Load” button on your PACS workstation, trying to load an OFFLINE study.

7.4.3

C-Find DIMSE

Just like with C-Store, the C-Find DIMSE works as a network transport to move C-Find IODs between various Application Entities. Consequently, its structure is not much different from the C-Store IOD, as you can see in Table 7.12.

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Table 7.12 C-Find-Rq Message field Command Group Length

Tag (0000,0000)

VR UL

Affected SOP Class UID

(0000,0002)

UI

Command Field Message ID Priority

(0000,0100) (0000,0110) (0000,0700)

US US US

Data Set Type

(0000,0800)

US

DICOM SOPs: Basic

Value/description The even number of bytes from the end of the (0000,0000) value field to the end of the C-Find-Rq message Contains SOP UID for this C-Find query root, that is, one of the following three: 1.2.840.10008.5.1.4.1.2.1.1 (Patient) 1.2.840.10008.5.1.4.1.2.2.1 (Study) 1.2.840.10008.5.1.4.1.2.3.1 (PatientStudy) 0020 Unique numerical ID for this message One of the following choices: 0002 (for low priority) 0000 (for medium priority) 0001 (for high priority) Anything different from 0101

Just like with the C-Store-Rq, the Data Set Type field (0000,0800) is not NULL (in DICOM NULL is 0101) and a C-Find-Rq message is immediately followed by the C-Find IOD, conveying search attributes as I noted earlier. This DIMSE + IOD couple travels on the network from the C-Find SCU Application Entity to the C-Find SCP Application Entity. The latter receives the C-Find DIMSE first, identifies it as such, then takes the C-Find IOD and searches its local database for matches. When matches are found, C-Find SCP replies with C-Find-Rsp messages, each complemented by a found C-Find IOD (Fig. 7.12 and Table 7.13). Note that, in general, we expect multiple matches to be found (e.g, all studies for a given patient). For each match (except the last one) the C-Find SCP replies with a DIMSE + IOD pair, where DIMSE has its status (0000,0900) set to FF00 (pending, work in progress) and IOD conveys the current match. The last match returns with the status set to 0000, thus indicating the successful completion of the operation. If nothing is found (no matches), C-Find replies with a single C-Find-Rsp message where Data Set Type (0000,0800) will be set to 0101 (NULL), and status (0000,0900) will be set to 0000 (success). If you are not quite so lucky and errors do occur, C-Find aborts with an error value in the Status field. In this case, as always, the DICOM Conformance Statement for the C-Find SCP (the device you were querying) should provide some interpretation for its supported status error codes. The most common places to look for errors include: 1. Incorrect DICOM configuration – For example, the AE of the C-Find SCU is not entered into the C-Find SCP configuration and is therefore rejected as illegal (the most common DICOM communication error).

7.4

Query: Find

145

C-Find SCU (Example: Workstation) C-Find Request: Find a list of all studies for patient with Patient ID 12345

C-Find Response: A list of IODs with requested Patient ID C-Find SCP (Example: Archive)

Fig. 7.12 DICOM C-Find Table 7.13 C-Find-Rsp Message field Command Group Length

Tag (0000,0000)

VR UL

Affected SOP Class UID

(0000,0002)

UI

Command Field Message ID Being Responded To

(0000,0100) (0000,0120)

US US

Data Set Type

(0000,0800)

US

Affected SOP Instance UID

(0000,1000)

UI

Status

(0000,0900)

US

Value/description The even number of bytes from the end of the (0000,0000) value field to the end of the C-Find-Rsp message Contains SOP UID for this C-Find query root, that is, one of the following three: 1.2.840.10008.5.1.4.1.2.1.1 (Patient) 1.2.840.10008.5.1.4.1.2.2.1 (Study) 1.2.840.10008.5.1.4.1.2.3.1 (PatientStudy) 8020 Shall be set to the value of the Message ID (0000,0110) field used in associated C-Find-Rq Message Anything different from 0101, if matched IOD is returned Contains the UID of the SOP Instance found 0000 (if successful), FF00 (if pending), or other values for warnings and errors

2. Incorrectly formed C-Find request IOD – Does not satisfy the hierarchical query rules; for example, if you are trying to search for a modality without knowing the study key attributes. 3. Mismatched query SOP – If an application sends a C-Find query on the Study root (value in (0000,0002) set to 1.2.840.10008.5.1.4.1.2.2.1) to an application that supports only Patient-root queries (1.2.840.10008.5.1.4.1.2.1.1 SOP SCP), C-Find will clearly fail. Once again, two AEs can communicate only if they act as SCU and SCP for the same SOP class. In the C-Find case, this is the same query level.

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4. Unsupported C-Find SCP – Do not expect that any DICOM device or application will support the C-Find SCP (will reply to queries). For example, many modalities may only be programmed to push images to the remote archives (C-Store). This would be their primary function, and they would not support any other DICOM service. As always, check the DICOM Conformance Statement for C-Find SCP support before you attempt to query the application. C-Echo is easy, and C-Find is only a bit harder, which you can clearly see from the C-Find DICOM encoding example provided in Appendix A.5. If you really want to get into all the DICOM bytes, check it out. As you can see, even at the C-Find level, DICOM messages are getting considerably more complex.

7.5

Modality Worklist

The Modality Worklist SOP (MWL), shown in Table 7.14, is derived from C-Find, yet it serves a totally different purpose. When modality operators (technologists) come to work, they really would like to know the current list of patients to scan, and even better, to have patient data already loaded into the modality unit (such as CT or MR scanner). This is exactly what the MWL SOP does: it preloads patient and scheduling information into the modalities. This places MWL at the very beginning of any DICOM workflow – as shown in Fig. 7.13 – well before images are stored or even scanned. To provide patient information and scanning schedules to the modalities, the MWL SCP needs to get them from somewhere. In the majority of cases, this information comes from the Radiology Information System (RIS) where the patients were already registered for imaging scans. RIS (or its substitute, used at a particular clinical location) is not DICOM-based – it uses the HL7 standard instead, as we will see in Chap. 14 – so either the MWL server or some additional DICOM broker converts RIS scheduling data into DICOM. In this manner, the MWL SOP (server) takes patient demographic and scheduling information and stores it in DICOM format. This is the time when MWL SOP communication kicks in, initiated by each modality. In the most common cases, modalities are configured to auto-query the MWL SCP server on a regular basis (every minute or so), fetching their part of the schedules (whatever is set for each modality). The query is performed with the C-Find-Rq message, using MWL SOP UID 1.2.840.10008.5.1.4.31 in the (0000,0002) tag. To filter the schedules for each particular modality device, modality type (CT, MR, US, and so on) and AE title (CTSCANNER1, for example) are used in C-Find-Rq-attached IOD. (IOD is shown in Fig. 7.11.) In response, the MWL SCP (server) employs a matching C-Find-Rsp (with the same MWL SOP UID) to return the identified study data to each modality. This creates a worklist on each modality: we know whom to scan, when, and how. Then the entire querying cycle repeats itself a minute or so later, making sure the modalities have the

7.5

Modality Worklist

Table 7.14 Modality Worklist SOP SOP class name Modality Worklist

147

SOP class UID 1.2.840.10008.5.1.4.31

Radiology Information System

Send current patient scanning schedules

MWL SCP (DICOM server or broker) Fetching patient/schedule info from RIS into MWL SCP, converting it into DICOM

C-Find-Rq: Find current scanning schedules for this modality

MWL SCU (modality)

C-Find-Rsp: Provide current scanning schedules

MWL SOP, based on C-Find DIMSE

Fig. 7.13 DICOM Modality Worklist example: populating imaging modalities with basic patient data

most recent scheduling information. In addition to this standard workflow, modality operators can also use manual search interfaces, as shown in Fig. 7.14, for more specific schedule updates. When the patient is scanned, all MWL-transferred data (such as Patient Name or DOB) is embedded into DICOM images, leaving no room for errors. What are the principal pros and cons of the MWL SOP? The main advantages are clear: • MWL eliminates manual data entry on the modalities, reducing the main source for human errors and wasted time. • Automatic fetching of MWL data directly from the RIS makes the entire process fast, clinically sound and robust. • MWL parameters such as pregnancy status or allergies, commonly supported by modalities, are essential for many types of radiological exams. The principal drawback of the MWL lies in its passive nature: modalities repetitively pull the MWL data from the MWL SCP server, and the latter has no ability to undo or delete retrospective transfers. Once patient/schedule information is passed to the modality, it is there, and it can be erased or edited only by the modality operator. In a busy clinic, patient/schedule information can change at a moment’s notice, and manually editing it on the modality after the initial MWL fetch defeats the purpose of MWL. How can you deal with this problem? Do not populate MWL lists days or weeks ahead of scanning time. I knew one clinic that tried to use MWL as their principal

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Fig. 7.14 DICOM Modality Worklist: sample interface (AlgoM server, www.algom.com) to retrieve MWL records from MWL SCP

patient scheduling engine, storing months of perspective scanning data into the modality worklists – bad idea! Instead, provide a “just-in-time” interface on the RIS side so MWL data can be sent to the MWL server right before the scan is needed. This approach minimizes the probability of cancelling or editing the scan information – and you will still benefit from the automatic, typo-free data flow.

7.5.1

MWL IOD

As you know, the Modality Worklist IOD contains information about the scheduled patient’s study. In its minimum form,9 it includes the most frequently used elements, as shown in Table 7.15. Note that the (0040,0100) “Scheduled Procedure Step Sequence” element is encoded here with SQ VR (see Sect. 5.3.10), that is, it includes a subsequence of items (0040,0001), (0040,0002), and more. This sequence encapsulates the schedule for the patient scan. When retrieved from the MWL SCP, DICOM sequence matching couples it to a particular modality and time interval. The Accession Number in (0008,0050) is used to bind the original study record in the Radiology Information System (where the Accession Number is usually generated) with its DICOM images 9

The complete Modality worklist attribute list can be found in PS3.4, Sect. K.6.1.2.2.

7.5

Modality Worklist

Table 7.15 MWL IOD (Group, Element) (0010,0010) (0010,0020) (0010,0030) (0010,0040) (0010,21 C0) (0008,0050) (0032,1032) (0008,0090) (0040,0100) >(0040,0001) >(0040,0002) >(0040,0003) >(0008,0060) >(0040,0006)

149

Name Patient Name Patient ID Patients Birth Date Patient’s Sex Pregnancy Status Accession Number Requesting Physician Referring Physician’s Name Scheduled Procedure Step Sequence Scheduled Station AE Title (name of the modality) Scheduled Procedure Step Start Date Scheduled Procedure Step Start Time Modality Scheduled Performing Physician’s Name

MWL SOP

DIMSE: C-Find

IOD Identifier: patient and scheduling data

Fig. 7.15 Modality Worklist SOP

acquired on the modality. After the Accession Number is stored in its (0008,0050) DICOM image attribute, it uniquely links RIS and PACS study records. As I mentioned, the list in Table 7.15 is minimal; more likely, a particular modality will require more data, which should be reflected in the modality’s DICOM Conformance Statement. If some of those specific attributes are not provided to the modality, it will likely abort the MWL data transfer, insisting on a complete attributes list. In other words, if you are working on establishing MWL connectivity, always start by revising the modality’s Conformance Statement. The list of attributes required for the modality should match those provided by the MWL SCP.

7.5.2

MWL DIMSE

The MWL DIMSE is nothing more than our good old friend C-Find DIMSE (see Sect. 7.4.3), which makes at least this part of the MWL implementation easy (Fig. 7.15). So no MWL-Rq and MWL-Rsp tables appear in this section – they are identical to the C-Find-Rq and C-Find-Rsp tables in 7.4.3. The only obvious change

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is in the (0000,0002) attribute value, which is now 1.2.840.10008.5.1.4.31 for the Modality Worklist SOP.

7.6

Basic DICOM Retrieval: C-Get

Let’s drift back a couple of chapters and recall what we learned about C-Store. If you had carefully read the description of the DICOM Storage SOP (used to transfer DICOM images) you might have been puzzled by a simple question: “How do we know what to store?” Indeed, the C-Store SOP transmits previously identified images one-by-one, but provides no means to decide what these images should be. Quite often, we do not need to think about image selection. For example, a CT scanner is usually set to transmit all newly acquired CT studies to the digital archive. Because the scanner will be producing those studies, it will know exactly what needs to be C-Stored. But what if we need to retrieve the most recent patient’s images from a digital archive? Basic DICOM image retrieval is accomplished with the C-Get SOP. Conceptually, C-Get blends C-Find and C-Store into a single service class where the required images can be identified based on a C-Find-like query, followed by a C-Store retrieval. Just like with C-Find, we form a C-Get request and we attach an IOD Identifier object with our image search criteria. When this request is sent to the C-Get SCP, the SCP first uses the search parameters to find the images then invokes C-Store to return them to the C-Get SCU, as illustrated in Fig. 7.16. Thus, a C-Get SCU (such as our image retrieving workstation in Fig. 7.16) must also act as a C-Store SCP to accept the images returned to it. As you can see, each single image matched on the C-Get SCP is wrapped in a separate C-Store operation and sent to the C-Get SCU (C-Store SCP). During this sending process, the C-Get SCP can also send C-Get responses with status set to pending to acknowledge C-Store sub-operations in progress. In these pending replies, the C-Get IOD reply also includes the currently matched values of the C-Get search attributes. When all storage operations are executed, the C-Get SCP replies with the final C-Get-Rsp response containing no IOD and signaling the end of the C-Get operation. If anything in this chain fails to comply, the entire C-Get operation is aborted with an appropriate error value entered in the C-Get status field. C-Get inherits three retrieval roots from C-Find as we see in Table 7.16. The most frequently used is the Study Root, meaning that we retrieve individual studies. As always, the C-Get SOP follows the same simple SOP layout with a DIMSE command and IOD data objects, as we show in Fig. 7.17.

7.6.1

C-Get IOD

The C-Get Information Object Identifier transmits the search attributes for the images to be retrieved. This makes it similar to the C-Find IOD; we provide attributes to be matched on the C-Get SCP end. Any image matching those attributes is

7.6

Basic DICOM Retrieval: C-Get

151

Get SOP Request: Retrieve all images for patient with Patient ID 123456 Find all images for Pat ID 123456

C-Get SCU (C-Store SCP) (Example: Workstation)

Store each found image back to the workstation Store SOP Request: Store first found image

Store SOP Response: Image number 1 stored

C-Get SCP (C-Store SCU) (Example: Archive)

C-Get SOP response: pending

Store SOP Request: Store last found image Store SOP Response: last image stored

Get SOP Response: Images retrieved

Fig. 7.16 Wrapping C-Store service in C-Get service Table 7.16 C-Get SOP SOP class name Patient Root Q/R Get Study Root Q/R Get Patient-Study Root Q/R Get (Retired)

SOP class UID 1.2.840.10008.5.1.4.1.2.1.3 1.2.840.10008.5.1.4.1.2.2.3 1.2.840.10008.5.1.4.1.2.3.3

C-Get SOP

DIMSE: C-Get

Fig. 7.17 C-Get SOP

IOD Identifier: patient data

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Table 7.17 C-Get IOD example (Group, Element) Name (0008,0052) Retrieve Level

(0010,0020) (0020,000D)

Example STUDY

Patient ID 12345 Study Instance UID 1.2.840.1234567

DICOM SOPs: Basic

Matching (see Sect. 5.3.2) Can be PATIENT, STUDY, SERIES, IMAGE. This element defines the level or hierarchical search Single value Single value or list matching

returned to us. Consequently, the attributes for C-Get should also conform to the hierarchical DICOM Patient-Study-Series-Image search logic just as in the case of C-Find. As you should glean from Table 7.17, one principal difference exists between the C-Find and C-Get IODs: the C-Get SCU must supply unique key values to identify an entity at the level of the retrieval. That is, instead of the required/optional key approach in C-Find, C-Get operates strictly on a hierarchical key level, using the unique keys for the current level and above to identify the images for retrieval. Therefore, the C-Get Identifier IOD must contain: 1. Query/Retrieve Level (0008,0052) – Defines the level of the retrieval. 2. Unique key attributes – May include “Patient ID” (0010,0020), “Study Instance UID” (0020,000D), “Series Instance UID” (0020,000E), and “SOP Instance UID” (0008,0018). The C-Get Identifier IOD must not contain any other optional key. All unique key attributes in C-Get must have exact values; that is, they will be matched with single value matching.10 The C-Get SCP generates a C-Get response for each match with an identifier (DICOM data object) that contains the values of all key fields and all known attributes requested.11 C-Get performs the image query-and-retrieve job admirably, but the hierarchical level-keys-only model is clearly very restrictive – maybe too much so to be considered user-friendly. Only one of the four level key attributes, Patient ID, is commonly used in medical interfaces and known to PACS users. The other three (Study, Series, and SOP Instance UIDs) are usually automatically generated by the modalities. They contain up to 64 characters, are never displayed to the users, have no particular meaning, and consequently never get typed in interactive interfaces. This means that a PACS user willing to use pure C-Get interactively can do it only on the patient level, retrieving all images for a given Patient ID. However, Patient-level retrievals are rare in

10

Study, Series, and Image levels may also support list matching, see Sect. 7.4.1. However, Patient ID must be unique. 11 As usual, there is also a relational (extended) C-Get behavior, rarely needed or implemented, which is supposed to work with any nonhierarchical, relational retrieval. Most DICOM Conformance Statements will contain a phrase “C-Get Extended Negotiation will be NOT supported by the SCP,” proving that any advanced features (relational searches included) are not very popular with DICOM manufacturers.

7.6

Basic DICOM Retrieval: C-Get

153

Step 1: start from the level with known level UID: Patient ID

C-Find Request: Find all images for patient with Patient ID 123456 and study date = today

C-Find Response: a list of today’s studies for patient ID 123456. Each returned study IOD contains Study Instance UID C-Get and C-Find SCU (Example: Workstation)

Step 2: knowing UID for the next level (Study), download studies with C-Get.

C-Get Request: Download all images for patient with Patient ID 123456 and Study Instance UID as returned by C-Find

C-Get and C-Find SCP (Example: Archive)

C-Get Response (via C-Store suboperations): loaded images for Patient ID 123456 and study date = today

Fig. 7.18 Combining C-Find and C-Get example: retrieving today’s images for a patient with known patient ID

radiology – we always start with the most recent studies and prefer to work on the study level. Besides, retrieving all images for a given patient might generate a huge data download. So, can you use C-Get anyway? Yes, provided that you use C-Find before you use C-Get. C-Get does not really support optional key matching, but C-Find does. For example, any system can run C-Find by Patient ID and Study date to locate the most recent patient study. When the C-Find SCP replies to this search, it also returns with C-Find-Rsp IODs containing just what C-Get needs: the unique key UIDs for the next level. For example, C-Find on the Patient level returns the Study instance UID. When this UID is known, it can be used in C-Get to retrieve the images in question, as shown in Fig. 7.18. Consequently, this approach is implemented in every single PACS. Users are first presented with a querying interface and always do a search (C-Find) first, before attempting to load anything. When search results are returned to the users, they also return those hidden hierarchical level UIDs that PACS software uses for subsequent

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C-Get downloads. In other words, C-Get is used for retrieval of the images for which level key attributes are already known.

7.6.2

C-Get DIMSE

The C-Get DIMSE is quite similar to C-Find and C-Store. C-Get-Rq is shown in Table 7.18. The (0000,0800) Data Set Type field is not NULL (0101) and the C-Get-Rq message is immediately followed by the C-Get IOD, conveying search attributes as we discussed. This DIMSE + IOD couple travels on the network from the C-Get SCU Application Entity to the C-Get SCP Application Entity. The latter receives the C-Get DIMSE first, identifies it as such, then takes the C-Get IOD and searches its local database for matches. When matches are found, the C-Get SCP replies with C-Get-Rsp messages; each complemented by a found C-Get IOD. Because C-Get has a C-Store operation embedded in it, the last four attributes in Table 7.19 for C-Get-Rsp are used to return the current statistics on a C-Store execution. They tell you how many images (individual C-Store sub-operations) are still left to transmit, how many were already transmitted, how many have failed, and how many executed with warnings. For example, if a C-Get SCP returns pending messages during C-Store execution, those numbers (sub-operation counts) reflect the progress of the entire image retrieval. C-Get warnings in most cases refer to some kind of acceptable deviations not leading to the entire C-Get failure (for example, if the C-Get SCP failed to support optional matching parameters). Failure to support any required parameter, on the other hand, would cause a C-Get error and failure. When all retrievals have been completed, the C-Get-Rsp delivers the final message. If everything goes okay, the message should have no data attached ( (0000,0800) set to 0101; (0000,1020) set to zero; (0000,1021) containing the total count of all images transmitted; and (0000,1022) set to zero as well).

7.7

Advanced DICOM Retrieval: C-Move

C-Move is practically identical to C-Get, with just a little complexity twist: you can move to third parties. That is, where C-Get can be used to return images only to the requesting Application Entity, C-Move can send (C-Store) them to any other AE (Fig. 7.19). Sure, in the simplest scenario an Application Entity can C-Move to itself, which means that it would do a C-Get-like download. For example, you can imagine that in Fig. 7.19 Workstation 2 is the same as Workstation 1. In reality, this is often the case: in most PACS, C-Move is used to do plain C-Get-like downloads, and this makes some sense. If Workstation 1 C-Moves Archive images to itself, we are at least sure that Workstation 1 is on, that it was able to issue a C-Move-Request, that it needs these images, and that it will more likely accept them. Moving to a real third party, such as another Workstation 2 located elsewhere, is a more daring endeavor.

7.7

Advanced DICOM Retrieval: C-Move

155

Table 7.18 C-Get-Rq Message field Command Group Length

Tag (0000,0000)

VR UL

Affected SOP Class UID

(0000,0002)

UI

Command Field Message ID Priority

(0000,0100) (0000,0110) (0000,0700)

US US US

Data Set Type

(0000,0800)

US

Table 7.19 C-Get-Rsp Message field Command Group Length

Tag (0000,0000)

VR UL

Affected SOP Class UID

(0000,0002)

UI

Command Field Message ID Being Responded To

(0000,0100) (0000,0120)

US US

Data Set Type

(0000,0800)

US

Status

(0000,0900)

US

Number of Remaining Sub-operations

(0000,1020)

US

Number of Completed Sub-operations

(0000,1021)

US

Number of Failed Sub-operations

(0000,1022)

US

Number of Warning Sub-operations

(0000,1023)

US

Value/description The even number of bytes from the end of the (0000,0000) value field to the end of the C-Get-Rq message Contains SOP UID for this C-Get query root, that is, one of the following three: 1.2.840.10008.5.1.4.1.2.1.3 (Patient) 1.2.840.10008.5.1.4.1.2.2.3 (Study) 1.2.840.10008.5.1.4.1.2.3.3 (PatientStudy) 0010 Unique numerical ID for this message One of the following choices: 0002 (for low priority) 0000 (for medium priority) 0001 (for high priority) Anything different from 0101

Value/description The even number of bytes from the end of the (0000,0000) value field to the end of the C-Get-Rq message Contains SOP UID for this C-Get query root, that is, one of the following three: 1.2.840.10008.5.1.4.1.2.1.3 (Patient) 1.2.840.10008.5.1.4.1.2.2.3 (Study) 1.2.840.10008.5.1.4.1.2.3.3 (Patient-Study) 8010 Shall be set to the value of the Message ID (0000,0110) field used in associated C-Get-Rq Message Anything different from 0101, if matched IOD is returned 0000 (if successful), FF00 (if pending), or other values for warnings and errors The number of remaining C-STORE sub-operations to be invoked for this C-GET operation The number of C-STORE sub-operations invoked by this C-GET operation that have completed successfully The number of C-STORE sub-operations invoked by this C-GET operation that have failed The number of C-STORE sub-operations invoked by this C-GET operation that generated warning responses

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Move SOP Request: Load study for patient with ID=123 on workstation 2

C-Move SCU (Example: Workstation 1)

C-Move SCP (Example: Archive)

Store request: Images for patient with ID=123

C-Move SCP (Example: Workstation 2)

Fig. 7.19 C-Move with three entities: Workstation 1 instructs Archive to send image to Workstation 2. Archive sends images to Workstation 2 with C-Store sub-operation

Because Workstation 2 is not requesting anything explicitly, it might not even be available – or ready – to deal with a stream of Archive images sent to it on a Workstation 1 request. Therefore, the complex, three-entity form of C-Move is usually used in more well-controlled environments – for example, a large multi-server PACS archive. A complex PACS archive can include several servers to store images,12 and several processing nodes to control this storage. In this layout, when an outside user requests archived images, one archive server can accept this request from the user, identify another archive server that contains the requested images, and ask that server to return the images. To the user, the entire process looks like the images are downloaded from a single, solid server when in fact it is processed with an internal C-Move. Another difference between C-Move and C-Get originates from the same threeentity principle employed in C-Move: C-Move needs to know where to send the images. Indeed, this question is never raised in C-Get; the C-Get SCP always returns the images to the image-requesting entity (C-Get SCU). In technical terms, C-Get works on a single association (we’ll talk more about this intriguing subject in Chap. 9): if I call you on a phone, you just pick it up and start talking to me; you don’t have to know my number to reply.

12

For example, those servers can be dedicated to specific modalities, locations, or time frames (short- and long-term archive storage).

7.7

Advanced DICOM Retrieval: C-Move

Table 7.20 C-Move SOP SOP class name Patient Root Q/R Move Study Root Q/R Move Patient-Study Root Q/R Move (Retired)

157

SOP class UID 1.2.840.10008.5.1.4.1.2.1.2 1.2.840.10008.5.1.4.1.2.2.2 1.2.840.10008.5.1.4.1.2.3.2

C-Move SOP

DIMSE: C-Move

IOD Identifier: patient data

Fig. 7.20 C-Move SOP

C-Move looks more like me calling you on a phone and asking you to call our friend John. Now you need to find out what John’s number is and whether you can call him. In DICOM terms, you need to open another association to speak with John. Sure, many bad things can happen to you during this intricate task. You might not find John’s number, or you might dial a wrong one. In DICOM terms, the Archive server in the previous illustration must have the correct Workstation 2 name, IP address, and port in its AE configuration list to be able to send images. In fact, even if the Archive needs to C-Move images back to Workstation 1 (twoentity C-Move, C-Get-like), it will need to know the complete Workstation 1 configuration and reply to it on another association (instead of just using the same, as C-Get). With our phone example, it works like this: I am calling you, you answer “Hello, wait a sec, I’ll call you back,” hang up and then call me back to continue our conversation. If you forget my number, we are stuck. This might look just like some cheap cell phone strategy, but this is precisely what C-Move does: it always opens another connection (DICOM association) to return its calls – the images. Moreover, nothing in network settings (such as a firewall) should block the outgoing connection from Archive to Workstation 2. Apart from this association business, C-Move is a very close C-Get relative. Certainly, it has three DICOM query/retrieve roots, each with its own SOP (Table 7.20). The SOP structure of C-Move is the same as everything else (Fig. 7.20).

7.7.1

C-Move IOD

A C-Move IOD DICOM object, shown in Table 7.21, transmits the search attributes for the images to be retrieved in the same “unique level keys only” way as we have with C-Get.

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Table 7.21 C-Move IOD example (Group, Element) Name (0008,0052) Retrieve Level

(0010,0020) (0020,000D)

Patient ID Study Instance UID

Example STUDY

12345 1.2.840.1234567

DICOM SOPs: Basic

Matching (see Sect. 5.3.2) Can be PATIENT, STUDY, SERIES, or IMAGE. This element defines the level or hierarchical search Single value Single value or list matching

Consequently – just like with C-Get – to make C-Move requests more practical and to support optional search keys, PACS user interfaces make us C-Find first to implicitly retrieve the unique level keys based on the original search. When the level keys are known, we can fire a full-fledged C-Move request. This may seem like overkill: as we learned in the beginning of our C-Get review, C-Get and C-Move are already designed as (C-Find) + (C-Store) pairs; and the limited (level-key-only) C-Find inside of C-Move and C-Get really forces us to use another real C-Find before a move or get just to identify the keys. Nevertheless, in real life the separate C-Find before the image transfer makes a whole lot of sense for two reasons: 1. It quickly answers the question of whether what we are looking for exists in the SCP archive. For example, if there is no today’s studies for the patient with the ID = 123456, then there is no need to C-Get or C-Move them somewhere. 2. If something is found, C-Find also replies to the question of how many; and this is important. For example, if we discovered that patient 123456 has multiple studies (5000 images in each) we would really think twice about which of them we want to download, if any at all. Loading images takes time – and sometimes considerable time. C-Find forces some decision making about what we really need and what we can afford in a reasonably short time period.13 You really do not need to worry about configuring C-Find before you use C-Move. The PACS user interface does this for you every time it asks you first to find what you want to move or download.

7.7.2

C-Move DIMSE

The C-Move DIMSE is very similar to C-Get as well, with one obvious addition for the move destination attribute as you can see in Table 7.22. In fact, PS3.7 of DICOM comes up with another entertaining name, “C-Get-Move service,” which only proves

13

Another typical example: a radiologist might need to look only at one specific series in a large multiseries study. Then, after locating the study, he would use another C-Find to get the list of all series in it, and then C-Move (or C-Get) to load the single series in question. Saves time and network traffic. In fact, some PACS would do series-level C-Find automatically for any study-level C-Find to get the list of series upfront.

7.7

Advanced DICOM Retrieval: C-Move

159

Table 7.22 C-Move-Rq Message field Command Group Length

Tag (0000,0000)

VR UL

Affected SOP Class UID

(0000,0002)

UI

Command Field Message ID Priority

(0000,0100) (0000,0110) (0000,0700)

US US US

Data Set Type Move Destination

(0000,0800) (0000,0600)

US AE

Value/description The even number of bytes from the end of the (0000,0000) value field to the end of the C-Move-Rq message Contains SOP UID for this C-Move query root, that is, one of the following three: 1.2.840.10008.5.1.4.1.2.1.2 (Patient) 1.2.840.10008.5.1.4.1.2.2.2 (Study) 1.2.840.10008.5.1.4.1.2.3.2 (Patient-Study) 0021 Unique numerical ID for this message One of the following choices: 0002 (for low priority) 0000 (for medium priority) 0001 (for high priority) Anything different from 0101 Shall be set to the DICOM AE Title of the destination DICOM AE to which the C-Store sub-operations are being performed

how easy it can be to confuse these two, even for those writing the DICOM standard. The new (compared to C-Get) Move Destination (0000,0600) attribute shown here specifies where the images need to be moved to by the C-Store sub-operation (Workstation 2 in our earlier example). When the C-Move SCP accepts this request, it will do the following: 1. Based on the unique level keys provided in the attached Identifier IOD, it will find the matching images in its own database. 2. For each of the images, it will issue a separate C-Store sub-operation request to store the images on the Move Destination AE. Note that only the AE title is provided for the C-Move-Rq destination. Based on this title, the C-Move SCP finds the remaining AE parameters (IP address, port number) in its configured C-Move destination list to open a second association with the destination AE (Workstation 2 in our Fig. 7.19 example). 3. As images are C-Stored on the destination AE, the C-Move SCP acknowledges the requesting AE (C-Move SCU) with the pending C-Move-Responses, just like we have seen with C-Get. In particular, those include the C-Store sub-operation counts (successful, failed, warnings, errors). 4. When the C-Store image transfer to the Move Destination AE is over, the C-Move SCP issues the final success C-Move-Rsp to the requesting C-Move SCU (or, if anything failed, an error C-Move-Rsp) – see Table 7.23. As we will learn in a moment, C-Cancel command can be used by the C-Move SCU to cancel the C-Move operation in progress.

160

7

Table 7.23 C-Move-Rsp Message field Command Group Length

Tag (0000,0000)

VR UL

Affected SOP Class UID

(0000,0002)

UI

Command Field Message ID Being Responded To

(0000,0100) (0000,0120)

US US

Data Set Type

(0000,0800)

US

Status

(0000,0900)

US

Number of Remaining Sub-operations

(0000,1020)

US

Number of Completed Sub-operations

(0000,1021)

US

Number of Failed Sub-operations

(0000,1022)

US

Number of Warning Sub-operations

(0000,1023)

US

7.7.3

DICOM SOPs: Basic

Value/description The even number of bytes from the end of the (0000,0000) value field to the end of the C-Move-Rq message Contains SOP UID for this C-Move query root, that is, one of the following three: 1.2.840.10008.5.1.4.1.2.1.3 (Patient) 1.2.840.10008.5.1.4.1.2.2.3 (Study) 1.2.840.10008.5.1.4.1.2.3.3 (Patient-Study) 8021 Shall be set to the value of the Message ID (0000,0110) field used in associated C-Move-Rq Message Anything different from 0101, if matched IOD is returned 0000 (if successful), FF00 (if pending), or other values for warnings and errors The number of remaining C-STORE sub-operations to be invoked for this C-MOVE operation The number of C-STORE sub-operations invoked by this C-MOVE operation which have completed successfully The number of C-STORE sub-operations invoked by this C-MOVE operation which have failed The number of C-STORE sub-operations invoked by this C-MOVE operation which generated warning responses

C-Move Versus C-Get

Because of the similar functionality of C-Get and C-Move, it is worth spending some time outlining their differences. The need for C-Move to know its destination is often presented as an essential security feature. If some evil hacker attempts to steal a few images, he will fail miserably because his evil workstations are not in the C-Move destination list (C-Get would reply to any requestor). Sending images only to known destinations is definitely a nice C-Move feature; it can indeed be used for security – and often is. Beyond the apparent practical security features, DICOM (in PS3.7) suggests that C-Get is practically archaic: It is expected that in most environments the C-Move is a simpler solution despite the fact that two associations are required. The use of the C-Get service may not be widely implemented. It may be implemented in special cases where a system does not support multiple associations. It was left in this version of the Standard for backward compatibility with previous versions of the Standard.

7.7

Advanced DICOM Retrieval: C-Move

161

So C-Get is supported only for the sake of backward compatibility? Can that really be? Calling C-Get archaic,14 or considering a single-association model inferior to a more “advanced” dual-association model, seems like a very strange move on DICOM’s part. C-Get, with its single association model, relieves the user from one of the most severe DICOM problems: a statically configured communication setup (where each entity needs to know ahead of time the list of its peers). If you could expect phone calls only from the people in your phone book, life would get a little bit dull, wouldn’t it? Plus, if anything changed, the phone book would need to be updated to make it work again. These so-called “secure” features in the DICOM world translate into a lack of dynamic functionality in real life, where being dynamic is essential for survival. With C-Get you can do teleradiology; you can connect to your DICOM devices from anywhere in the world; you can use mobile units; and you can rebuild your network any way you like without being concerned about updating the “phone books” on every C-Move SCP. With C-Move, you are stuck in a very static world. Even a little disaster (such as your system administrator playfully changing a few IP addresses) can ruin the entire DICOM network. In the world of communications, any new link or association needs additional maintenance and creates additional overhead, which can potentially fail. PACS security should be based on functionality and on the ability to withstand hits; and not on the manual maintenance. The single-association C-Get model adds another degree of freedom: it can work with firewalls (Fig. 7.21). Consider the following, very typical scenario: you need to read images from a remote unit or archive. You know their AE settings, you can connect to them, you can even query them, but when you try to retrieve data with C-Move, it fails. The reason is simple: C-Echo (for connectivity verifications), C-Find (for querying), C-Get (for retrievals) work on single associations. When you initiate a single-association connection from inside your firewalled environment (such as your hospital or even your firewalled computer), most firewalls would let this connection work – outbound requests are usually considered benign. However, C-Move response implies the remote system coming to you with another, inbound association – and this will be blocked by the firewall. Thus, C-Get can be even more secure than C-Move, because it does not require drilling firewall holes, and can coexist with your security policies.15 On the other hand, C-Move (as I have mentioned) is much more appropriate in complex, self-contained, multi-server PACS archives where image storage and processing loads are distributed between multiple archive computers on a local network. In those well-controlled, occluded environments changes are rare because

14

The “It was left in this version of the Standard” mantra repeats itself in all recent DICOM revisions, year after year, without having any real effect on C-Get popularity. Maybe someone should update it finally. 15 Sure, VPN would be even better, but they usually take more time to set up.

162

7

DICOM SOPs: Basic

C-Get with remote archive

C-Get Request -> N ^ n ~ / ? O _ o

Name (0010,0010), and run them through a language-conversion routine – readily available on any computing platform. However, even this step becomes a challenge for most DICOM application developers, as can be seen in Fig. 16.2. But this little task of proper language display only scratches the surface of the far deeper internationalization issues. First of all, what is the origin of the correct (0008,0005) value? When radiologists or receptionists type a patient name in a Greek hospital, they do not really click

346

16

DICOM Applications: Usual and Not

A

B

C

Fig. 16.2 Testing DICOM (0008,0005) with different DICOM software. Application A displayed all patient names correctly, using their respective character sets. Application B stumbled on some languages; application C was not prepared to handle foreign characters at all, replacing them with question marks

on some “use Greek encoding” button to apply the proper character repertoire. The language value most likely comes from two principal sources: s Language settings in the program interface (when programs come in localized, language-specific versions) s Language settings on the computer (such as “Region and Language” in Windows) You would expect them to be perfectly right and in sync, and you are certainly right – in theory. In reality, the more a hospital grows, the more it is exposed to a mixture of various regional and language settings. Our imaginary Greek hospital can buy a modality unit from a US manufacturer, and innocently forget to change its default Latin repertoire. The same hospital can buy a plain PC from a French company, and again forget to set regional settings to Greece. The very same hospital can expand its operations to an international affiliate in China, and import a few Chinese-encoded DICOM files from there… And in each of these cases, before it gets noticed the hospital PACS will be inundated with incorrectly localized patient data, virtually impossible to fix. As radiology goes global, so do the devices and data that radiologists mix in their practices; and their localization capabilities quite often are not equal to the task.

16.2

DICOM Localization

347

Lost in Translation

I recently dealt with a Russian hospital where we were installing new PACS software from a US-based company. As soon as everything was connected and DICOM data began to flow into the PACS archive, we suddenly faced a serious localization problem. In the PACS archive, all Russian patient names were converted into gibberish, which is not a local dialect. Meanwhile, the same names were showing up just fine on the hospital’s DR unit (digital X-ray modality) manufactured in Russia. So the “foreign” PACS was hastily blamed as incompatible. However, when we dug a bit dipper, we discovered a totally different origin of the problem. The original DR images did not have the proper (0008,0005) value set to the Russian (Cyrillic) ISO_IR 144 character set. In fact, they did not have this tag at all, which meant that the system was using the default Latin repertoire. But why were Russian patient names displaying correctly in the DR’s interface? Simply because it was running on a Russian-localized computer in which the Cyrillic encoding had been applied to all names by default. You may recognize the problem: similar things happen with web browsers or plain text (.txt) files, when pages in different languages may display fine on some PCs and look like gobbledygook on the others.

What I just described is still a local problem. Now let us think about shipping the data between hospitals. Consider a Greek hospital sending its “Greek” DICOM data (attribute (0008,0005) set to ISO_IR 126) to a German facility where their Greek patient happens to be treated. In theory, the (0008,0005) should be enough to force Greek characters even when imported into a German PACS. In reality, this will rarely happen because it has never been planned for or tested. Worse still, consider a German radiologist typing in German text (annotations, reports, study descriptions, structured reports), which will then propagate to the original Greek DICOM data set; now we are climbing the Tower of Babel. And last but not least, think about querying the German PACS archive for a Greek patient, using the patient’s name. What language would you use to begin with? Although DICOM plans for this, insisting on using (0008,0005) in its query objects, how would this work in reality when multiple encodings collide? The patient name will be stored in the PACS database is some language; if it is written in another language in the query object, matching these two names is not a trivial task. “We do not care about names, we match patient IDs,” you might reply – as you refer to Sect. 5.6.1: and you are perfectly right. But guess what; patient ID (0010,0020) is a text string as well (with LO VR), and can be written in Greek, German, or any other character repertoire! As DICOM puts it, “implementations may be confronted with character sets that are unknown to them,” and in international DICOM data exchange, this is often the case. In short, DICOM’s character repertoire is a very lame and antiquated solution to data localization: they store the (0008,0005) language tag separately from the data

348

16

DICOM Applications: Usual and Not

strings it needs to be applied to. When two things are stored separately, they will diverge sooner or later – you can surely add this to Murphy’s list of laws. The only practical solution to language-specific repertoires would be to use Unicode. Unlike repertoires (storing language values separately from the data they apply to), Unicode makes the language flag part of the character value, which usually means using more than a single byte per letter. In this case, the correct language can be recovered from the character string itself, without being dependent on some extraneous (0008,0005) tags. And one can mix Unicode characters in all possible ways without getting lost in the languages. Unicode has been the de facto standard in computer application internationalization for some time now. For example, all current operating systems use Unicode, which enables you to use any language you choose without being dependent on the OS localization (thus, you can have files with Greek and Chinese names in a German version of Windows). DICOM caught up with this trend, but only partially; it recognizes Unicode as a valid character repertoire (see Table 16.1). However, this recognition is somewhat ironic. While Unicode is self-sufficient for representing any language selection, DICOM still needs it in (0008,0005) just to ensure that we are not using the default Latin alphabet. Unfortunately, Unicode-aware DICOM products are rare, and those that do exist do not exhibit any exciting growth tendencies. All hospitals in countries with Latin or nearly Latin alphabets stay with the default Table 16.2; others struggle with their native-only language approaches; and only a very few plan for multilanguage projects. Before this situation improves, most international DICOM projects are stuck with transliteration: typing everything in the Latin alphabet based on a “sounds like” approach (5.3.2). Unfortunately, things like foreign names, uniquely spelled in their native languages, often produce obscure tongue-twisting monstrosities when written in Latin; but compared to converting names between different character repertoires, Latin transliteration look like the lesser of two evils.

Lost in Translation 2

When you take the “sounds like” approach, think about not surprising your foreign colleagues with unusual combinations. What sounds normal to you may be confusing to them. Think about Toyota MR2, which had to be renamed in France because of “MR2” sounding just too similar to “merde.”

For many international clinics, the first encounter with the DICOM (0008,0005) tag happens during the most basic language-related project: ensuring native language support at their own facility. By now you should be cautious enough not to take this for granted. If you want to have native language workflow at your hospital, then:

16.2

DICOM Localization

349

s Make sure all your medical devices can support the language of choice. To verify this, you do not have to dig into their DICOM settings (although this might be informative). Just test them using DICOM objects with correct (0008,0005) values, such as DICOM objects from your international partner, or public character repertoire object sets like the one from here: http://dclunie.com/images/ charset/. The names in the chosen language should show correctly in the interface of all your devices and software (Fig. 16.2). s Make sure all your computers can support the language of choice and are set to that language. s Make sure all your input devices (such as keyboards) are set with the chosen language. s For devices incapable of supporting the chosen language, develop automated transliteration software that can convert your language strings into DICOM default Latin (Table 16.2) and back. Quite often, there will be ambiguities in this conversion, which is why it is important to make it as automatic as possible, thereby removing the subjective and error-prone “human factor.” s For places where manual transliteration (such as typing patient data into a Latinonly device or program) must occur, distribute correct transliteration tables and make sure that your staff is using them consistently. s Make sure your transliteration can be uniquely decoded, especially when converting back from Latin to your native language. Should “sh” in some patient names be pronounced as “s”+ “h”, or “sh”? You may need to use uppercase letters and/or punctuation signs (whatever you find in Table 16.2) to avoid these ambiguities. s For important patient identifiers (such as Patient IDs), use language-independent numeric strings. Your best bet in any language-specific project would be to reduce misspellings to a bare minimum, and relieving your staff from doing any manual string-conversion tasks.

Lost in Translation 3

I have always been impressed by the number of Latin spellings for my Russian last name, used in my Russian documents, issued by the Russian authorities. Would not you hope that at least each country could decide on how it transliterates for the international documents? Wrong. I have been through “French,” “English,” phonetic, and possibly Martian spellings of my last name – all issued by the same office at different times. Every time they came up with something particularly unusual and creative, the answer to my “Why?” was always the same: “This is the new computer program we are using.” Therefore, if you transliterate your non-Latin characters, try to find the most standard and least ambiguous transliteration, and stick to it. Otherwise, your patients will change names every time “the new computer program” is deployed.

350

16.3

16

DICOM Applications: Usual and Not

Radiation Dose Monitoring

An average CT scan exposes the patient to 400 times the radiation of a simple chest X-ray – bad news! Worse news: radiation accumulates in our bodies, and too much can increase the risk of cancer and other diseases. Worse news still: the never-ending quest for higher resolution imaging inevitably leads to higher radiation doses. As a result, we have created a vicious circle: to prevent cancer, one needs more frequent and higher-resolution scans, which means more radiation for the patient, which leads to higher cancer risks. What can be done about it, and how can DICOM help? Clearly, the entire problem of optimizing X-ray technology (CT, mammography, conventional X-rays) lays outside the DICOM realm. Scores of brilliant engineers earn their bread and butter perfecting X-ray devices. Nonetheless, tracking patient irradiation events becomes the key to controlling radiation dose. When the total dose can be assessed correctly, it can be managed; and we can know ahead of time how many more scans a patient can tolerate without running into health problems. Simple? As always, only on paper. Consider the main source of current imaging radiation: the CT scanner. In theory, its per-body-volume radiation measure – known as the Weighted Volume CT Dose Index (CTDIvol) – should be proportional to the scanner current recorded in DICOM (0018,9332) “Exposure in mAs” attribute. In theory as well, for each CT series, one should be able to multiply this by the number of images (proportional to the series scan length) to obtain the total radiation Dose-Length Product (DLP). Viewing each CT series as an “irradiation event,” one can sum up their individual DLPs, resulting in total DLP for the entire CT exam. In reality, everything becomes way more complex. First of all, the human body is not uniform; different organs and tissues accumulate radiation differently. Second, modern CT scanners try to optimize (modulate) radiation dose, adjusting to human body nonuniformities to produce the best image. Third, scanner radiation output depends on many internal device settings along with the way the patient’s body is positioned and moved inside of the scanner. So what is the bottom line? Forget about different devices and vendors: CT scanning internals become so convoluted that even two identical scanners from the same vendor need to be calibrated to ensure consistent dose distribution and output. As a result, simplistic attempts to predict radiation exposure from patient weight, size, or CT current tend to produce inaccurate values, which are hardly acceptable for patient exposure tracking. This means that the only way to accurately know the dose is to get it from the scanner, based on its internal calibration and settings. But once dose tracking becomes scanner-dependent, we immediately venture into a wonderland of proprietary problems, with different vendors using different units and formats for dose representation. Therefore, when radiation overexposure started gaining attention a few years ago, the only solution that some CT vendors provided was adding a

16.3

Radiation Dose Monitoring

351

Fig. 16.3 (Left) Example of dose report image from GE Discovery CT750HD scanner. For each scanned series, Weighted Volume CT Dose Index (CTDIvol) and Dose-Length Product (DLP) are shown, along with the total exam DLP. (Right) DICOM tags for the same image, displayed with nonproprietary software. You can see the mixture of standard and unreadable values, but the total report does show in the highlighted (0040,0310) tag

radiation report image to the original CT scan sequence (Fig. 16.3). The image would show total radiation doses used for each scanned series, based on some phantom calibration used for the scanner. Well, the report image was better than nothing, but what could one do with this image in a busy hospital? All dose numbers need to be stored in a database, instead of being presented in a screenshot. Grabbing them from the image pixels would require OCR (Optical Character Recognition), which is complex and inefficient in the clinical context. Instead of the screenshot reports dead-end, more efforts were expected from DICOM to standardize dose reporting in a legible, easy-to-automate format. Steps in this direction have become more apparent in the recent DICOM editions. Part 3.3 of the DICOM standard introduces the Radiation Dose module (see Table C.4-16 in DICOM PS3.9), shown here in Table 16.3. Note that (0040,0310) attribute was used to report radiation dose for the CT scan from Fig. 16.3 (selected line in the right image). Storing dose data in the standardized DICOM tags was already better than screenshots, and many were able to take advantage of it. Nonetheless, image-based data reporting suffered from several problems. First, DICOM defined most of these tags as optional, which resulted in different tag support at different vendors. Second, storing study-specific information in individual SOP instances is not robust by definition. Some images can be removed, manipulated, or replaced; others can be sent to the PACS server before the study is finished (therefore containing incomplete dose data).

352

16

DICOM Applications: Usual and Not

Table 16.3 Radiation dose module Attribute name Tag Attribute description Anatomic (0008,2229) Anatomic structure, space, or region that has been exposed to Structure, ionizing radiation. The sequence may have zero or one Items Space, or Region Sequence >Include ‘Code Sequence No Baseline Context ID is defined Macro’ Table 8.8-1 Total Time of (0040,0300) Total duration of X-Ray exposure during fluoroscopy in Fluoroscopy seconds (pedal time) during this Performed Procedure Step Total (0040,0301) Total number of exposures made during this Performed Number of Procedure Step. The number includes nondigital and digital Exposures exposures Distance (0018,1110) Distance in mm from the source to detector center. Note: Source to This value is traditionally referred to as Source Image Detector Receptor Distance (SID) Distance (0040,0306) Distance in mm from the source to the surface of the patient Source to closest to the source during this Performed Procedure Step. Entrance Note: This may be an estimated value based on assumptions about the patient’s body size and habitus Entrance (0040,0302) Average entrance dose value measured in dGy at the surface Dose of the patient during this Performed Procedure Step. Note: This may be an estimated value based on assumptions about the patient’s body size and habitus Entrance (0040,8302) Average entrance dose value measured in mGy at the surface Dose of the patient during this Performed in mGy Procedure Step. Note: This may be an estimated value based on assumptions about the patient’s body size and habitus Exposed (0040,0303) Typical dimension of the exposed area at the detector plane. Area If Rectangular: row dimension followed by column; if Round: diameter. Measured in mm. Notes: 1. This may be an estimated value based on assumptions about the patient’s body size and habitus. 2. This attribute is used in the X-Ray Acquisition Dose Module with units in cm (see Section C 8.7.8 Table C.8-33) Image and (0018,115E) Total area-dose-product to which the patient was exposed, Fluoroscopy accumulated over the complete Performed Procedure Step Area Dose and measured in dGy*cm*cm, including fluoroscopy. Notes: Product 1. The sum of the area dose product of all images of a Series or a Study may not result in the total area dose product to which the patient was exposed. 2. This may be an estimated value based on assumptions about the patient’s body size and habitus Comments (0040,0310) User-defined comments on any special conditions related to on Radiation radiation dose encountered during this Performed Procedure Step Dose Exposure (0040,030E) Exposure Dose Sequence will contain Total Number of Dose Exposures (0040,0301) items plus an item for each fluoroscopy Sequence episode not already counted as an exposure

16.3

Radiation Dose Monitoring

Table 16.3 (continued) Attribute name Tag >Radiation (0018,115A) Mode >KVp (0018,0060) >X-Ray Tube (0018,8151) Current in PA >Exposure (0018,1150) Time >Filter Type (0018,1160)

>Filter Material

(0018,7050)

>Comments on Radiation Dose

(0040,0310)

353

Attribute description Specifies X-Ray radiation mode. Enumerated Values: CONTINUOUS PULSED Peak kilo voltage output of the x-ray generator used. An average in the case of fluoroscopy (continuous radiation mode). X-Ray Tube Current in PA. An average in the case of fluoroscopy (continuous radiation mode) Time of X-Ray exposure or fluoroscopy in ms Type of filter(s) inserted into the X-Ray beam (for example, wedges). See C.8.7.10 and C.8.15.3.9 (for enhanced CT) for Defined Terms The X-Ray absorbing material used in the filter. May be multivalued. See Sects. C.8.7.10 and C.8.15.3.9 (for enhanced CT) for Defined Terms User-defined comments on any special conditions related to radiation dose encountered during the episode described by this Exposure Dose Sequence Item

Therefore, in the few past years, a more comprehensive radiation dose reporting started making its way into the DICOM: Radiation Dose Structured Reports (RDSR), detailed in PS3.16, and DICOM Supplements 127 and 150 (Fig. 16.4). The use of RDSR makes more sense than sprinkling study-wide dosage data over the individual DICOM images. Fortunately, it seems that the concept of RDSRs is getting gradually popular with modality vendors, and will hopefully provide a unified dose reporting mechanism in the future. At the same time, anything about structured reporting is still news to the PACS vendors, and most current PACS lack sufficient (if any) SR support. This adds another layer of implementation complexity, so if you want to go with RDSRs, get in touch with your modality and PACS manufacturers to make sure they are up-to-date with your expectations. Finally, all DICOM can offer is a radiation report (dose) for the study in question. If you need to track older patient irradiation events, you inevitably need another software application in which DICOM reports from each scan will be stored to compute cumulative patient doses. But then, you must consider the problems of patients travelling between various hospitals (where their irradiation events were not recorded or were recorded differently), retrospective scans, unreported scans, and so on. One way to solve this problem could be mobile patient records: patients carrying their medical information on smart cards or other portable media. The most critical patient data, radiation dose included, can be stored there cumulatively and backed up to a safe location at each hospital visit. This is still very much a “dream technology,” but the recent advances in mobile and cloud processing can make it a reality. In short, radiation dose tracking needs work – within DICOM and outside – to provide standardized data and interfaces for reliable dose records.

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DICOM Applications: Usual and Not

Fig. 16.4 Using PixMed SR DoseUtility tool to display the contents of Radiation Dose SR; http://www.dclunie.com/pixelmed/software/webstart/DoseUtilityUsage.html

And now? If you are concerned about robust radiation dose tracking (as you should be), keep pressure on your CT and X-ray vendors; ask for DICOM dose reporting. At least this will help all of us move in the right direction.

Next Time…

A couple of years ago my colleagues and I were charged with setting an automated dose reporting system in which radiation dose values would be read directly from scanned DICOM images. We went to several CT scanner manufacturers and waded through many layers of their engineering and technical

16.4

DICOS

355

support just to find out that none of them provided any standard dose reporting. Adding a dose report image to a CT scan was considered state-of-the-art then, but the mere idea of running OCR against a PACS archive sent shivers down our spines. So we asked where the numbers came from and were told some vague stories about the raw data reports nested deeply into proprietary scanner architecture. Were these numbers accurate? Who knows! And when we asked about using DICOM for making radiation data available, the answer was the same as for any other nonexisting functions: “We do not have it right now, but we will have it in our next revolutionary model.” With a little luck, this situation will soon change and robust radiation dose tracking with DICOM will become commonplace on all irradiating devices. Keep in mind that without standardized dose reporting, one cannot even tell that the radiation numbers in some proprietary formats are accurate.

16.4

DICOS

It is interesting to see how DICOM finds unusual applications in seemingly remote areas such as airport security (having nothing to do with the security we just discussed). Digital Imaging and COmmunications in Security (DICOS) provides an example of DICOM expanding well beyond the original medical realm. As much as we are already accustomed to the use of X-rays for security checks, new elaborate security threats call for more elaborate detection tools, which can be borrowed from digital medicine. DICOS makes an attempt to adjust the DICOM object model and attributes to the needs of security checks (Fig. 16.5). For its own needs, DICOS changes the DICOM information model to the hierarchy of Owner (highest level), Object of Inspection (OOI), Scan, and Series of objects. When John Smith walks with his suitcases through an airport security gate, he, his suitcases, and his laptop bag become the OOIs. Note that John may or may not be the Owner of some of these items, which makes him different from a DICOM Patient who definitely owns all his body parts. For the rest, DICOS scan corresponds to DICOM Study, and Series (data obtained on a particular scanning device) to DICOM modality-specific Series. Modality (scanning device) can vary from traditional X-ray or CT to less usual devices such as security trace detectors or even manual inspection. TDR, or Threat Detection Report (for Series, Scan, OOI or Owner), including information regarding the properties, locations, and probabilities of suspicious objects, becomes the ultimate outcome of the DICOS data analysis. It is interesting to note that DICOS stresses the use of intelligent computer algorithms to detect and evaluate threats. Initial threat detection (image regions, ROI, containing potential threats), threat classification (such as assignment of threat probabilities to each detected ROI), and final threat aggregation (collecting all threats to make an overall assessment of the security risk) can hardly be done “by

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Owner Aggregate Owner TDR OOI

OOI OOI TDR

Scan

ScanTDR

Scan

Series

Series

Series

Fig. 16.5 DICOS information model

hand” without extensive use of computerized data analysis. This puts DICOS very close to clinical CAD (Computer-Aided Diagnosis): both rely on intelligent number crunching to estimate and measure the importance of certain image details. DICOS inherits another important DICOM functionality: data transmission. The same SOP model (see Sect. 7.2) is used to transmit and store DICOS data, whether it is CT images or Threat Detection Reports. On the other hand, some DICOM functions such as information encryption and protection still have to make their way to DICOS. The introduction of “see-through” devices in airports has already raised understandable concern about passenger privacy. Verbal assurances that the security images will be deleted and will not be distributed illegally

16.5

Coloring DICOM Images: Science and Art

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certainly do not work in the age of digital permissiveness; and if patient confidentiality is not protected by the standard, it will not be protected at all. In general, if you think about DICOM, the only “medical” part of it is its Data Dictionary, which translates clinical attributes into otherwise purely abstract objects. In other words, scanning for guns and explosives can be implemented with the same DICOM tools used for scanning tumors and plaques. When we see DICOS using DICOM for security, we can easily imagine that one day some DICOS tools (trace detectors and image-processing algorithms perhaps) may as well find some other clinical use. All rivers run into the sea…

16.5

Coloring DICOM Images: Science and Art

Grayscale images, films, and monitors has ruled medical imaging for ages; and I can remember that even just 10 years ago many of my radiologist friends would consider any color as sheer distraction. This was somewhat expected: most of the core DICOM modalities – CT, MR, ultrasound, and X-ray – were meant to image one-dimensional data, such as tissue density; there was nothing to color. Slowly but surely the dull grayscale landscape of medical imaging has started to change, and DICOM has had to catch up. If the color is not in the original DICOM data, where might it come from? From any kind of postprocessing, increasing the original data dimensionality. Volumetric imaging is a perfect (and quite old) example of enhancing monochrome DICOM images with color. Think about complex 3D or even 4D (3D plus time) renderings, when multiple anatomic structures and organs overlap in a single image. Doing them with grayscale would hide most structures behind the others, barely conveying any information. Color is another story: even the three basic color channels (Red, Green, and Blue) provide you with an infinite number of mixtures, sufficient to differentiate between the most subtle shades. Adding a fourth palette channel – Alpha transparency – allow us to make objects semitransparent, providing another layer of visual complexity. Coloring called for palettes: tables, mapping original imaging data (such as grayscale densities) into different colors. Consistently with two-byte/pixel DICOM grayscale, DICOM palettes are also two-byte per color channel, meaning that palette color data may range from (0,0,0) minimum to (65535,65535,65535) maximum (R,G,B). Compare this to (0,0,0) to (255,255,255) in bitmap/JPEG images and conventional monitors (Fig. 16.6). Complex image postprocessing was only one of the many ways to introduce color. As another example, consider new dual-energy CT: the images are obtained simultaneously at two different energy levels, yet they are often presented in a single picture. Adding another energy dimension changes the old CT density display (Hounsfield units, HUs); different materials with the same HUs look different in dual-energy visualization: so color naturally shows the mixtures of different “basis” materials (such as iodine and water). Think about functional imaging, when certain processes (blood flow, tumor growth, brain impulses) have to be visualized on top

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Fig. 16.6 Color in CT volume rendering helps visualize different structures, otherwise overlapping in grayscale or hidden behind the other anatomy. Images by Anders Persson (NewScientist) and Maryellen O’Rourke (BIDMC Advanced Imaging Lab)

of the usual imagery – color becomes the only meaningful way to make it possible. Consider hybrid imaging, when images from different modalities – PET and MR, PET and CT, MR and CT, and so on – are fused together; one needs color to differentiate between the image types. I think you get the idea: whether the color is present in the original data or not, it may be essential for informative image display. Therefore, during the past 2 years DICOM has made several steps to enrich its color portfolio. Standard color palettes (Hot metal, Hot iron, PET, and such) finally made their debut in the DICOM Data Dictionary (Fig. 16.7). It was a good move: before palette standardization, each vendor implemented coloring on its own, often making the results incompatible with the others. Moreover, in 2009 DICOM added color palettes to its Information Model (PS3.3), treating them as separate entities meant to colorize otherwise monochrome data. Consequently, the Color Palette IOD was introduced (section A58 in PS3.3) to handle the palette lookup table – the table that maps the original grayscale values into RGB colors. To manage palettes, DICOM now offers Color Palette Storage and Query/ Retrieve SOPs (Table 16.4, see DICOM PS3.4, Annex W and X) so that DICOM applications can search and send palettes to each other, just like they search and send the images.

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Fig. 16.7 Playing with Gioconda and popular PACS palettes. Hot iron, Hot, French, Spectral, Thermal, Grayscale Table 16.4 Service classes to manage color palettes in DICOM SOP class name SOP class UID Color Palette Information 1.2.840.10008.5.1.4.39.1 Model – STORE Color Palette Information 1.2.840.10008.5.1.4.39.2 Model – FIND Color Palette Information 1.2.840.10008.5.1.4.39.3 Model – FIND Color Palette Information 1.2.840.10008.5.1.4.39.4 Model – FIND

Technically, Color Palette Storage and Query/Retrieve SOPs work just like image-exchanging SOPs: we simply extend C-Find, C-Get, C-Move, and C-Store to palette IODs. Consider palette storage: the interacting AEs (SCP and SCU with

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Fig. 16.8 Coloring original grayscale image with spectral palette. Note better segmentation of different tissue intensities

respect to palette storage SOP) start with the usual association negotiation and follow the usual C-Store protocol (see Sect. 7.3); but instead of image IODs, palette IODs are attached to C-Store DIMSE messages. DICOM does not define (yet), what Color Palette Storage SCU is supposed to do after it receives the palette; it is left to the application developers (DICOM Conformance Statement). You may add this palette to your application interface, apply it to current images, or do something else altogether (Fig. 16.8). La vie en rose

Color often comes from various image-processing options provided in DICOM software. This has one important implication: if you see a colorful image in your DICOM software, it does not mean that the original image data has color as well. Quite often, the original might be in plain grayscale. I have seen many cases when radiologists were puzzled, sending colorful images from one workstation to another just to discover that the second workstation shows them in grayscale. “Where did all my colors go?” The answer is simple: grayscale data remained grayscale, and even if it was displayed in color by some software, opening it on another workstation will still produce a grayscale view.

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These tricks are particularly frequent in nuclear and functional imaging, where applications add default colors to the original data to highlight certain processes. Your best bet in preserving this coloring would be taking image screenshots (DICOM SC images, see Sect. 8.2), recording the images just as they appear on the screen.

Fig. 16.9 X-ray art – X-ray flowers by Hugh Turvey, X-ray Genus Python by LUßO, X-ray kiss, X-ray seashells by Bert Myers, X-ray leaves by Albert Koetsier (XRayArt)

Speaking of color, I would be remiss if I did not mention the most fascinating application of medical imaging: X-ray art and photography. What started as a pure entertainment for artistically inclined MDs has taken on a life of its own, becoming the preferred tool for many professional artists. I cannot wait for DICOM to be taught in art colleges (Fig. 16.9).

DICOM Software Development

17

You can teach me how to build a software to view medical images (eg CT, MRI …) DICOM standard was not. Response is expected soon. Thanks. – Original email from a reader.

So, you are thinking about writing your own DICOM software? Very ambitious of you – bon courage! After spending some 15 years in DICOM software development myself, I can admire your determination and hope that it will bear you golden fruits. If you sense some light sarcasm in these words, please do not take it to heart. I truly wish you success, and I really wanted to write parts of this book to help you with this difficult task. Just one important, overall piece of advice: know what you are doing. There are two principal ways of developing DICOM software: starting from scratch and using predeveloped libraries. Each approach has well-known pros and cons. In fact, even in my previous life as a DICOM software designer, I was constantly switching and mixing the two depending on the current needs and priorities. Let me see if I can help you learn from some of my mistakes.

17.1

Developing DICOM from Scratch

If your brain power seriously outweighs your budget, you might be tempted to develop your DICOM program from scratch. It makes sense. Prêt à porter commercial DICOM libraries are expensive (a few thousand dollars) and worse yet, they come with royalties – that is, you will have to pay the developers for each instance/ installation of your product that uses their libraries. Besides, you might not like their design and implementation; and in fact, you might be totally right. Commercial DICOM libraries often suffer from the same problems as all major PACS software products: they are often heavy, outdated, and inefficient. In fact, guess what major PACS software is typically built from? O.S. Pianykh, Digital Imaging and Communications in Medicine (DICOM ), DOI 10.1007/978-3-642-10850-1_17, © Springer-Verlag Berlin Heidelberg 2012

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Moreover, most commercial libraries started somewhere many years ago, and in many cases they began as re-packaged free-source or research products, inheriting errors and bugs from their originals (as we discussed in Sect. 12.6). As they became more commercial and mainstream, their evolution did not necessarily keep pace. Oftentimes, the more they dominated the market, the less flexible they became. Reasons enough for self-development, n’est pas?

17.2

The “I Just Need to Open this DICOM File” Project

All DICOM projects start for a reason. The most common is getting an image out of a DICOM file. In fact, I have already mentioned a simple trick of extracting all image bytes from the end of the DICOM file (see Sect. 11.1). Not bad, but it clearly has nothing to do with DICOM software design. And although needing a DICOM file opener is the most common reason for starting DICOM development, it is also the worst one. You tend to think small, neglect the entire structure of the DICOM standard, and partial success can create the illusion of doing it right. I really hope that after reading most of this book you realize that the DICOM standard expands much farther than a simple MR file viewer, even if the latter opens all files from your MR scanner. As a result, the more files you try to open, the more disappointing surprises and failures you will experience: constantly running into unknown pixel encodings, compressions, and IODs. In short, you will have to discover the DICOM standard empirically – by learning from your own mistakes. This is the most inefficient way of mastering something as complex and evolving as DICOM. In fact, by the time you make your next discovery, the actual items in DICOM and everywhere around you could change. You will never catch up. So, if you “just need to open this DICOM file,” use Matlab, Analyze, or some free DICOM file viewer to solve your problem in a matter of minutes and not days. Be practical.

17.3

Implementing DICOM

If you have decided to move forward and implement DICOM software – and not for some small transient need – I respectfully salute you. Go ahead and start from this book; it was written for you. Read Chap. 5, and while reading it, try to build a clear object-oriented design of your DICOM implementation. If I were you, I would consider each of the following guidelines carefully: 1. First of all, implement a good class for a binary data buffer, paying particular attention to efficient memory allocation and cleanup. Remember, DICOM reads, writes, and processes all its data as sequences of binary bytes, and this class will be responsible for the efficient management of all your DICOM data (Fig. 17.1). Include functions such as Big/Little Endian byte swapping and reading/writing to a file/socket.

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class BufferChunk { unsigned char* m_Data; // data bytes unsigned int m_Length; // may be constant …. } class Buffer { std::vector …. }

m_Data;

Fig. 17.1 Example of binary buffer class implementation. The most typical (and efficient way) is to implement the buffer as an array of (fixed-length) buffer chunks; proper choice of the chunk size optimizes memory allocations

class VR { unsigned short unsigned short Buffer …. unsigned int …. }

m_Group, m_Element; m_Type; // “AE”, “PN”, and other VR types m_Data; // actual data bytes GetVRNumber()

{ return m_Element+m_Group